Witold Pedrycz

EAAI Journal 2026 Journal Article

Correntropy meets cross-entropy: A robust loss against noisy labels

• Nan Zhou
• Qing Deng
• Wenjun Luo
• Xiuyu Huang
• Yuanhua Du
• Badong Chen
• Witold Pedrycz

Noisy labels are a common challenge in real-world datasets, severely degrading the training of deep learning models. Enhancing the robustness of the loss function offers a flexible solution to mitigate this issue. This study first demonstrates that Categorical Cross-Entropy (CE), one of the most popular choices used to train a classification model, leads to significant performance degradation. To alleviate this issue, we innovatively propose a novel loss function called Correntropy-Inspired Cross-Entropy (CICE) loss, which utilizes the properties of correntropy and is robust to noisy labels. Compared with CE, CICE retains CE’s core functionality for linear class separation while automatically alleviating the adverse effects of noisy labels during training. Extensive experiments on four public datasets across multiple scenarios with varying noisy label rates validate CICE’s effectiveness. Results show that CICE outperforms 13 state-of-the-art loss functions in noise resilience and classification accuracy, establishing its superiority in noisy-label environments.

AAAI Conference 2026 Conference Paper

On Coresets for End-to-end Learning from Crowds

• Hang Yang
• Zhiwu Li
• Witold Pedrycz

Crowdsourcing is a common approach for training data-hungry models by collecting high-quality labeled data with human labor. With crowdsourcing data, the end-to-end learning paradigm is rising, where the classifier is concatenated with annotator-specific confusion layers and the two parts are co-trained in a parameter-coupled manner. However, learning with the size of a very large set of annotations is a challenge when computation or energy is limited. In this paper, we analyze and refine the coresets for end-to-end learning from crowds under the sensitivity sampling framework. This coreset is a small possible subset of annotations, so one can efficiently optimize the Coupled Cross-Entropy Minimization problem with guaranteed approximation. We first prove the lower bound, which shows no coresets smaller than complete data with confusion layers. Then, with workers' transition matrices, we show that with the regularization term, this lower bound can be prevented. Our main result is that under mild assumptions, a smaller coreset exists for the regularized Coupled Cross-Entropy Minimization problem. An upper bound of sensitivity is proposed for designing a sampling algorithm called CrowdCore. The experimental results on synthetic and real-world datasets demonstrate the effectiveness of our analysis.

EAAI Journal 2025 Journal Article

A codebook-driven approach for low-light image enhancement

• Xu Wu
• Xianxu Hou
• Zhihui Lai
• Jie Zhou
• Ya-nan Zhang
• Witold Pedrycz
• Linlin Shen

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations; (2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging discrete codebook priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. The code can be obtained from https: //github. com/csxuwu/CodeEnhance or https: //www. scholat. com/laizhihui. cn.

EAAI Journal 2025 Journal Article

A decomposition–integration interval prediction strategy for iron ore shipping freight rates with reinforcement learning

• Hongyue Guo
• Yijia Zhang
• Yating Yu
• Lidong Wang
• Peng Jia
• Witold Pedrycz

The fluctuations in iron ore shipping freight rates significantly affect market participants’ investment decisions. This study proposes a “decomposition–integration” interval prediction method for iron ore shipping freight rates. First, the trend, seasonal, and residual components of the original data are extracted. Then, the residual component with high-frequency fluctuations is predicted by developing four interval prediction models with influencing factors. Reinforcement learning-derived dynamic weighting integrates prediction outputs to reveal the underlying data relationships. Finally, with point prediction outputs on the trend and seasonal components, the interval prediction is achieved by integrating these components predictions. The empirical results illustrate that the proposed method outperforms several benchmark methods in terms of prediction interval coverage probability of 61. 29% and deviation of 0. 02428, indicating its effectiveness in interval prediction.

EAAI Journal 2025 Journal Article

A robust fuzzy twin support vector machine with kernel-target alignment for binary classification

• Deepak Gupta
• Barenya Bikash Hazarika
• Umesh Gupta
• Witold Pedrycz

Many algorithms similar to the twin version of the support vector machine and their variants have shown better results in the binary classification of nonlinear data points. But in the presence of outlier and noise, these algorithms exhibit low generalization efficiency. To alleviate this challenge, recently proposed, kernel-target alignment based fuzzy least square twin bounded support vector machine (KTA-FLSTBSVM) used fuzzy membership values and is solved using least squares. Inspired by this strategy, for further improvement, we propose a novel approach called decision support kernel-target alignment based fuzzy least square twin bounded support vector machine (DS-KFIFTBSVM). DS-KFIFTBSVM considers the kernelized fuzzy membership values with the regularized twin support vector machine and solves for linear and nonlinear data points using a functional iterative approach. In DS-KFIFTBSVM, the solution is obtained by solving a linearly convergent iterative scheme rather than solving quadratic programming problems. The proposed DS-KFIFTBSVM offers better generalization efficiency, which has been evaluated using both linear and Gaussian kernels, mostly on artificially developed and publicly accessible datasets with diverse dimensionalities. In terms of various performance evaluation metrics, including specificity, precision, false positive rate, rate of misclassification error, F_score, and geometric mean in linear and non-linear cases, DS-KFIFTBSVM outperforms various baseline approaches. It shows the highest accuracy in various datasets, including 98. 1884 % for musk dataset (linear kernel) and 100 % for the glass dataset (Gaussian kernel). Further statistical analysis confirms its classification efficiency.

IJCAI Conference 2025 Conference Paper

Adaptive Deep Learning from Crowds

• Hang Yang
• Zhiwu Li
• Witold Pedrycz

In the data-driven era, collecting high-quality labeled data requiring human labor is a common approach for training data-hungry models, called crowdsourcing. Recently, end-to-end learning from crowds has shown its flexibility and practicality. However, existing works in an end-to-end manner focus on learning after collecting labels, which results in noisy annotations and also requires cost. Inspired by computerized adaptive testing, we argue that the characteristics of workers should be mined as soon as possible to make the best use of talents. To this end, we propose an adaptive learning from crowds method, AdaCrowd, as a cost-effective solution. Specifically, we propose a probabilistic model to capture the informativeness of possible instances for each worker. The informativeness is considered to be the uncertainty of the annotation prediction model output in its current status. The adaptive learning procedure is optimized by maximizing data likelihood and can be used with existing crowdsourcing models. Extensive experiments are conducted on real-world datasets, LabelMe and CIFAR-10H. The experimental results, e. g. , the reduction of annotations without performance degradation, demonstrate the effectiveness.

NeurIPS Conference 2025 Conference Paper

An Adaptive Quantum Circuit of Dempster's Rule of Combination for Uncertain Pattern Classification

• Fuyuan Xiao
• Yu Zhou
• Witold Pedrycz

In pattern classification, efficient uncertainty reasoning plays a critical role, particularly in real-time applications involving noisy data, ambiguous class boundaries, or overlapping categories. Leveraging the advanced computational power of quantum computing, an Adaptive Quantum Circuit for Dempster’s Rule of Combination (AQC-DRC) is proposed to address efficient classification under uncertain environments. The AQC-DRC is developed within the framework of quantum evidence theory (QET) and facilitates decision-making based on quantum basic probability and plausibility levels, which is a generalized Bayesian inference method. The AQC-DRC provides a deterministic computation of DRC, ensuring that quantum fusion outcomes in uncertain pattern classification are exactly aligned with those of the classical method, while simultaneously achieving exponential reductions in the computational complexity of evidence combination and significantly improving fusion efficiency. It is founded that the quantum basic probability amplitude function in QET, as a generalized quantum probability amplitude, can be naturally utilized to express the quantum amplitude encoding. In addition, the quantum basic probability in QET, as a generalized quantum probability, naturally forms a quantum basic probability distribution and can be used to represent quantum measurement outcomes for quantum basic probability level decision-making. Furthermore, the quantum plausibility function in QET also can be naturally used to express the quantum measurement outcomes for quantum plausibility level decision-making. These findings enrich the physical understanding of quantum amplitude encoding and quantum measurement outcomes, offering broad application prospects for representing and processing uncertain knowledge in pattern classification.

EAAI Journal 2025 Journal Article

Dynamic localization based-utility decision approach under type-2 Pythagorean fuzzy set for developing internet of modular self-reconfiguration robot things

• Nahia Mourad
• A.A. Zaidan
• Hassan A. Alsattar
• Sarah Qahtan
• B.B. Zaidan
• Muhammet Deveci
• Dragan Pamucar
• Witold Pedrycz

The Internet of Modular Robot Things (IoMRT) has emerged through the integration of robotic systems into the Internet of Things (IoT), offering a wide range of solutions to meet continuously growing demands. Six self-reconfiguration functionalities/criteria have been proposed for developing IoMRT. However, no study has fully developed an IoMRT that satisfies all the necessary functionalities. Additionally, there is a lack of scholarly research proposing a decision-based approach for evaluating and ranking IoMRT, which highlights a significant research gap. A complex multiple-criteria decision-making (MCDM) problem has arisen in evaluating and ranking IoMRT due to the diversity of functionalities, the need to prioritize these functionalities based on their importance, and data variability. To address this issue, the study proposes a novel decision-based approach for evaluating and ranking IoMRT, which consists of three phases: (i) Developing a novel weighting method called T2PFS-FWZICbIP (Type-2 Pythagorean Fuzzy Set–Fuzzy Weighted Zero Inconsistency based on Interrelationship Process) to measure the importance of the identified functionalities; (ii) Formulating a decision matrix by cross-referencing potential IoMRT developments with the six self-reconfiguration functionalities resulted in the selection of a random sample of 50 IoMRTs as proof of concept. Following this, the DLbU (Dynamic Localization-based Utility) method was proposed, integrating dynamic localization and utility procedures to manage binary data within the decision matrix; (iii) Developing a novel ranking method, T2PFS-DNMA (Type-2 Pythagorean Fuzzy Set–Double Normalization-based Multiple Aggregation), to address the diversity of functionalities and concerns regarding data variance. The results revealed that the Distributed functionality (C1) received the highest weight value of 0. 4060 according to T2PFS-FWZICbIP, indicating its high importance in the ranking of IoMRT. In contrast, the High-Fidelity functionality (C5) received a weight value of 0. 0733, indicating its very low importance in the ranking. IoMRT2 and IoMRT35 were identified as the most and least favored, respectively, according to T2PFS-DNMA. The robustness of the proposed approach was assessed through sensitivity analysis and comparative studies.

EAAI Journal 2025 Journal Article

Dynamical multiple polynomial-based neural networks classifier realized with the aid of dropfilter and dual statistical selection

• Zhen Wang
• Sung-Kwun Oh
• Zunwei Fu
• Seok-Beom Roh
• Witold Pedrycz

Polynomial neural networks (PNN) have emerged as an effective regression modeling methodology in computational intelligence, relying on its interpretable polynomial nodes to fit complex nonlinear data relationships and the adaptive nature of self-organizing networks. To break the bottleneck of PNN structure in the field of multi-classification, this study designs a dynamical multiple polynomial-based neural networks (DMPNN) classifier, focusing on developing a flexible polynomial network classification methodology that enhances predictive capabilities without sacrificing the advantages of PNN structures. Our approach effectively addresses the challenges of multi-class classification with uncertain class boundaries and reduces computational complexity, which is achieved through the synergy of several proposed techniques. Three key issues underpin the proposed DMPNN: (a) The integration of PNN regression models using the one-against-all strategy can provide effective and scalable solutions to multi-class classification problems, especially for uncertain class boundary issues. (b) The dual statistical selection (DSS) approach aims to eliminate redundant inputs during data processing, reduce the computational burden, and increase the variety of neural network nodes in the model neuron selection stage. (c) The synergy of regularization methods including the ℓ2 norm-based method (ℓ2-LSM) and the DropFilter, is exploited to mitigate potential overfitting in coefficient estimation and enhance the generalization capabilities of the proposed classifier. A series of ablation experiments and parameter analysis were conducted to demonstrate the stability and reliability of the proposed model. Then, we applied DMPNN to 17 publicly available datasets and two engineering applications: Recycling of black plastic wastes and phased resolved partial discharge. The performance results show that the DMPNN model outperforms five classical classifiers and four state-of-the-art (SOTA) classifiers on 78. 94% of the datasets. This highlights the unique ability of the proposed DMPNN to enhance predictive accuracy while maintaining model simplicity and interpretability.

EAAI Journal 2025 Journal Article

Extended multi-kernel relevance vector machine optimized Kriging interpolation for coal seam thickness prediction in coal-bearing strata

• Luefeng Chen
• Mingdi Ma
• Min Wu
• Witold Pedrycz
• Kaoru Hirota

During the drilling process of a coal mine roadway drilling rig, coal seam thickness variation affects the efficiency of coal seam mining. However, the cost of geological drilling required for coal seam exploration in practical engineering is high, the sample size of the data is small and the distribution is discrete, and spatial interpolation is required for coal seam thickness prediction in unexplored coal seams. Therefore, this paper proposes an improved method of kriging spatial interpolation for small sample, acquired from geological drilling. Firstly, for the small sample problem, we use a Relevance Vector Machine (RVM) to reconstruct the variogram in kriging interpolation. Secondly, multi-kernel RVM (MKRVM) is used to improve the fitting effect in global and local, respectively. Finally, Particle Swarm Optimization (PSO) is used as an extension of MKRVM to optimize the hyperparameters in the multiple kernel functions and the weights among different kernel functions to improve the fitting effect of the overall model. Through a series of comparative experiments, the superiority of the extended multi-kernel RVM (EMKRVM) method proposed in this paper is verified. At the same time, the method is applied to a practical project, and the results illustrate that our method has lower error in the prediction of coal seam thickness variation in coal-bearing strata, which can provide a better reference basis for the subsequent adjustment of drilling speed, rotation speed, and drilling pressure.

EAAI Journal 2025 Journal Article

Fuzzy granular computing for evaluating average uncertainty in machine learning models

• Naimeh Sadeghi
• Nima Gerami-Seresht
• Witold Pedrycz
• Aminah Robinson Fayek

Realistic evaluation of uncertainty is crucial for informed decision-making in machine learning (ML) models. Our study introduces a novel approach to quantify uncertainty in model predictions using fuzzy numbers. Fuzzy numbers capture the inherent vagueness and imprecision in real-world data by providing a flexible framework for expressing uncertainties. In contrast to the conventional approach of constructing prediction intervals by assuming Gaussian probability distributions, which results in symmetric confidence intervals, our method can develop prediction intervals that are asymmetrically centered around the numeric prediction results. The approach involves forming triangular fuzzy intervals around ML predictions, where interval shape parameters are optimized using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to maximize coverage while maintaining specificity. To the best of our knowledge, this is the first model-agnostic approach using fuzzy numbers to generate asymmetric prediction intervals. Validation on benchmark datasets demonstrates the method’s effectiveness and suggests that asymmetric intervals better capture predictive uncertainty. Results across three public datasets demonstrate that our method produces competitive or superior uncertainty estimates compared to standard techniques, particularly under data degradation scenarios such as added noise or reduced output resolution (‘coarsening’). ” A case study on industrial construction project duration estimation illustrates the method’s applicability. This case study highlights the practical value of our method in construction project duration forecasting, where distinguishing between over- and under-estimation is essential for budgeting, scheduling, and risk management.

TIST Journal 2025 Journal Article

Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning

• Cuiying Huo
• Dongxiao He
• Yawen Li
• Di Jin
• Jianwu Dang
• Witold Pedrycz
• Lingfei Wu
• Weixiong Zhang

Heterogeneous graph neural network (HGNN) is a popular technique for modeling and analyzing heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and time-consuming. Self-supervised contrastive learning has been proposed to address the problem of requiring annotated data by mining intrinsic properties in the given data. However, the existing contrastive learning methods are not suitable for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e.g., meta-path) in graph data while ignoring noises in node attributes and graph topologies. We develop a robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidances of node attributes and graph topologies and integrates and enhances them by a reciprocally contrastive mechanism to better model heterogeneous graphs. In this new approach, we adopt distinct but suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately. We further use both attribute similarity and topological correlation to construct high-quality contrastive samples. Extensive experiments on four large real-world heterogeneous graphs demonstrate the superiority and robustness of HGCL over several state-of-the-art methods.

EAAI Journal 2024 Journal Article

A manifold intelligent decision system for fusion and benchmarking of deep waste-sorting models

• Karrar Hameed Abdulkareem
• Mohammed Ahmed Subhi
• Mazin Abed Mohammed
• Mayas Aljibawi
• Jan Nedoma
• Radek Martinek
• Muhammet Deveci
• Wen-Long Shang

Increases in population and prosperity are linked to a worldwide rise in garbage. The “classification” and “recycling” of solid waste is a crucial tactic for dealing with the waste problem. This paper presents a new two-layer intelligent decision system for waste sorting based on fused features of Deep Learning (DL) models as well as a selection of an optimal deep Waste-Sorting Model (WSM) based on Multi-Criteria Decision Making (MCDM). A dataset comprising 1451 samples of images of waste, distributed across four classes – cardboard (403), glass (501), metal (410), and general trash (137), was used for sorting. This study proposes a Multi-Fused Decision Matrix (MFDM) based on identified fusion score level rules, evaluation criteria, and deep fused waste-sorting models. Five fusion rules used in the sorting process and the evaluation perspectives into the MFDM are sum, weighted sum, product, maximum, and minimum rules. Additionally, each of entropy and Visekriterijumska Optimizacija i Kompromisno Resenje in Serbian (VIKOR) methods was used for weighting selected criteria as well as ranking deep WSMs. The highest accuracy rate of 98% was scored by ResNet50-GoogleNet- Inception based on the minimum rule. However, under the same rule, an insufficient accuracy rate of sorting was presented by ResNet50-GoogleNet-Xception. Since Qi = 0 for Inception-Xception, the final output based on MCDM methods indicates that the fused Inception-Xception model outperforms the other fused deep WSMs, which achieved the lowest values of Qi. Thus, Inception-Xception was chosen as the best deep waste-sorting model based on images of waste, multiple evaluation criteria, and different fusion perspectives. The mean and standard deviation metrics were both used to validate the selection findings objectively. The suggested approach can aid urban decision-makers in prioritizing and choosing an Artificial Intelligence (AI)-optimized optimal sorting model.

EAAI Journal 2024 Journal Article

Accelerating the integration of the metaverse into urban transportation using fuzzy trigonometric based decision making

• Muhammet Deveci
• Dragan Pamucar
• Ilgin Gokasar
• Luis Martinez
• Mario Köppen
• Witold Pedrycz

Metaverse is defined as a fictional universe that could serve as a simulation environment of reality. Beginning in the past with games, it becomes increasingly integrated into human life as time passes. Metaverse usage is inevitable in every aspect of life. One of its potential application areas could be urban transportation. A novel fuzzy trigonometric based on the combination of the Full Consistency Method (FUCOM) and Combined Compromise Solution (CoCoSo) is proposed to rank three alternatives with twelve criteria under four major aspects: managerial, safety, user, and urban mobility. In the first stage, fuzzy FUCOM methods are used to calculate the weights of the criteria. In the second stage, the fuzzy trigonometric based CoCoSo method is applied to evaluate and rank the alternatives. The proposed model enables the nonlinear processing of complex and uncertain information using fuzzy trigonometric functions. The findings demonstrate focusing on a particular age group can make it easier to integrate the metaverse with urban transportation. The findings of this study have the potential to serve as a guide for decision-makers. The metaverse-based applications could be started by policymakers, which is a promising opportunity with potential boundaries beyond human comprehension making this statement weaker.

EAAI Journal 2024 Journal Article

An improved generative adversarial network to oversample imbalanced datasets

• Tingting Pan
• Witold Pedrycz
• Jie Yang
• Jian Wang

Many oversampling methods applied to imbalanced data generate samples according to local density distribution of minority samples. However, samples generated by these methods can only present a non-deterministic relationship between the local and global distributions. A generative adversarial network (GAN) is a suitable tool to learn an unknown global probability distribution. In this paper, we propose an improved GAN (I-GAN) to oversample according to the global underlying structure of minority samples. The originality of I-GAN stems from the fact it provides additional density distribution information of minority samples for GAN and generated samples. By building on this idea, three detailed strategies are presented: input random vectors of the generator are sampled from a rough estimate of the distribution of minority samples to orientate fake samples more believable; a residual about minority samples is added into the discriminator to strengthen the constraint of loss function; generated samples are redistributed with a reshaper. These three strategies provide innovative methodologies at various stages of GANs for the oversampling task. Compared with 22 classical and popular imbalanced sampling methods under metrics of G m, F 1, and A U C on 24 benchmark imbalanced datasets, it is shown that I-GAN is effective and robust. The I-GAN implementation line procedure has been uploaded to Github (https: //github. com/flowerbloom000/I-GAN).

EAAI Journal 2024 Journal Article

Assessing growth potential of careers with occupational mobility network and ensemble framework

• Jiamin Liu
• Tao Wang
• Feng Yao
• Witold Pedrycz
• Yanjie Song
• Renjie He

The growth potential of a career reflects its future prospects and is an important consideration for individuals and organizations when career planning. There is still a lack of quantitative assessment tools for growth potential of careers. In this study, considering the key role of human capital in human resource management, as well as the excellent performance of complex network and machine learning in big data analysis and prediction, a career growth potential assessment model with human capital ensemble is proposed through human capital-based occupational mobility network and ensemble learning. First, an occupational mobility network is constructed based on online professional dataset to associate occupations with each other. Then, five dimensions of human capital measurements are designed to quantify human capital in terms of education, experience, social capital, occupational size, and concentration. These are then combined with the occupational mobility network to create a new network that depicts human capital flows among occupations. Finally, an ensemble framework for assessing career growth potential is constructed to integrate multidimensional human capital information in the network and obtain quantitative scores of growth potential. This study is the original attempt to adopt a data-driven idea and an intelligent approach to understand career growth potential. The experimental results show that it also makes a useful exploration for modeling human capital flows and intelligent assessment of career prospects.

EAAI Journal 2024 Journal Article

Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving

• Shoaib Azam
• Farzeen Munir
• Ville Kyrki
• Tomasz Piotr Kucner
• Moongu Jeon
• Witold Pedrycz

Learning contextual and spatial environmental representations enhances autonomous vehicle’s hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack global environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such as historical data, situational subtleties, and behavioral predictions of other road users to form a rich contextual understanding of their surroundings. This neural map-based comprehension is integral to making informed decisions on the road. In contrast, even with their significant advancements, autonomous systems have yet to fully harness this depth of human-like contextual understanding. Motivated by this, our work draws inspiration from human driving patterns and seeks to formalize the sensor fusion approach within an end-to-end autonomous driving framework. We introduce a framework that integrates three cameras (left, right, and center) to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. The sensor data is fused and encoded using a self-attention mechanism, leading to an auto-regressive waypoint prediction module. We treat feature representation as a sequential problem, employing a vision transformer to distill the contextual interplay between sensor modalities. The efficacy of the proposed method is experimentally evaluated in both open and closed-loop settings. Our method achieves displacement error by 0. 67 m in open-loop settings, surpassing current methods by 6. 9% on the nuScenes dataset. In closed-loop evaluations on CARLA’s Town05 Long and Longest6 benchmarks, the proposed method enhances driving performance, route completion, and reduces infractions.

EAAI Journal 2024 Journal Article

Theory guided Lagrange programming neural network for subsurface flow problems

• Jian Wang
• Xiaofeng Xue
• Zhixue Sun
• Jun Yao
• El-Sayed M. El-Alfy
• Kai Zhang
• Witold Pedrycz
• Jacek Mańdziuk

A deep learning model can perform efficient uncertainty quantification (UQ) for reservoir flow with uncertain model parameters, but usually requires large amounts of training data to ensure accuracy. However, the cost of obtaining large amounts of data is prohibitive, and the performance will deteriorate if sufficient training data is lacking. Alternatively, more interpretable neural networks with embedded physical laws have recently been used to solve partial differential equations as well as to solve UQ problems. This approach has received a lot of attention due to its low data volume requirements and its adherence to the laws of physics during the training process. In this paper, we propose a theory-guided framework based on a bilevel programming model with hard constraints to embed physical meaning in the model. Theory guided Lagrange programming neural network (TGLPNN) combines the method of Lagrange programming neural network approach where physical laws such as stochastic partial differential equations and boundary conditions are incorporated into the training process of a convolutional neural network. At the same time, the upper-level variables are iteratively optimized. The method based on Lagrange programming neural network inherently embeds physical laws in the network. Practical applications have shown that TGLPNN can provide higher prediction accuracy compared to state-of-the-art physics-driven methods and improved efficiency compared to numerical methods.

EAAI Journal 2023 Journal Article

Data preprocessing strategy in constructing convolutional neural network classifier based on constrained particle swarm optimization with fuzzy penalty function

• Kun Zhou
• Sung-Kwun Oh
• Witold Pedrycz
• Jianlong Qiu

Convolutional neural networks (CNNs) have attracted increasing attention in recent years because of their powerful abilities to extract and represent spatial/temporal information. However, for general data, its features are assumed to have weak or no correlation, and directly applying CNN to classify such data could result in poor classification performance. To address this problem, a combined technique of original data representation method of fuzzy penalty function-based constrained particle swarm optimization (FCPSO) and CNN, so-called FCPSO-CNN is designed to effectively solve the classification problems for generic dataset and applied to recognize (classify) black plastic wastes in recycling problems. In more detail, CPSO is introduced to optimize feature reordering matrix under constraints and the construction of this matrix is driven by fitness function of CNN that quantifies classification performance. The Mamdani type fuzzy inference system (FIS) is employed to realize the fuzzy penalty function (FPF) which is utilized to realize the constrained problems of CPSO as well as alleviate the issues of the original penalty function method suffering from the lack of robustness. Experimental results demonstrate that FCPSO-CNN achieves the best classification accuracy on 13 out of 17 datasets; the statistical analysis also confirms the superiority of FCPSO-CNN. An interesting point is worth to mention that some feature reordering matrices in the infeasible space come with better classification accuracy. It has been found that the proposed method results in more accurate solution than one-dimensional CNN, random reordering feature-based CNN and some well-known classifiers (e. g. , Naive Bayes, Multilayer perceptron, Support vector machine).

EAAI Journal 2023 Journal Article

Information measures based on similarity under neutrosophic fuzzy environment and multi-criteria decision problems

• Quang-Thinh Bui
• My-Phuong Ngo
• Vaclav Snasel
• Witold Pedrycz
• Bay Vo

The neutrosophic fuzzy set (NF-set) is a unique hybrid structure that combines the essence of the fuzzy and neutrosophic sets. It is an effective mathematical tool for dealing with indeterminate and inconsistent information in situations where the data are imprecise or vague. This paper defines the concepts of similarity-based information measures, including entropy and cross-entropy, between NF-sets. It is the first time these concepts have been introduced since the NF-sets were defined. We provide their interesting properties through well-proven theorems. In addition, we also propose a novel and efficient algorithm to make multi-criteria decision(s) using these information measures with a clear step-by-step illustration. It overcomes the limitations of the original algorithm when evaluating the criteria in two aspects: qualitative and quantitative. A real-world experiment is then conducted to assist students in choosing the optimal subject group(s) for the Vietnamese national high school graduation examination. Experimental results show that our method outperforms the previous original method when giving recommendations with sixteen correct cases, three acceptable cases, and one noisy case out of twenty real-life cases. Finally, the experimental results are presented visually, analyzed rigorously, and discussed carefully, intending to verify the validity and feasibility of the proposed algorithm. The Python source code for experiments is publicly available on Github.

EAAI Journal 2023 Journal Article

Revisiting the consistency improvement and consensus reaching processes of intuitionistic multiplicative preference relations

• Rui Wang
• Zhen-Song Chen
• Bin Shuai
• Luis Martínez
• Wen-Tao Kong
• Witold Pedrycz

Intuitionistic multiplicative preference relation (IMPR) has been successfully and widely used to model the decision makers’ preferences elicited by pairwise comparisons. As the critical links of group decision-making (GDM) procedure, the consistency improvement process (CIP) and consensus reaching process (CRP) have been investigated intensively; nevertheless, the extant methods explored the CIP and CRP of IMPRs without considering the situations of whether decision makers participate in the reassessment task. To overcome the limitation, this paper proposes two novel IMPR-based GDM approaches with CIP and CRP under different situations. First, when decision makers only express their original preferences, the novel consistency index and group consensus measure of IMPRs are defined to construct the CIP and CRP without feedback mechanism, in which the original preferences can be updated automatically. Second, when decision makers are required to modify their opinions, a series of consistency, consensus, and proximity degrees are put forwards to generate the identification and direction rules for improving the inconsistent and conflict preferences. Subsequently, the induced intuitionistic multiplicative ordered geometric averaging operator is constructed to fuse the individual preferences for determining the GDM results, in which the collective IMPR can maintain the acceptable consistency. Finally, a numerical example is applied to illustrate the feasibility and advantages of the proposed methods, which can improve the consistency and consensus levels of IMPRs according to the degrees of decision makers’ involvement in the re-evaluation process.

EAAI Journal 2023 Journal Article

Solving energy-efficient fuzzy hybrid flow-shop scheduling problem at a variable machine speed using an extended NSGA-II

• Yi-Jian Wang
• Gai-Ge Wang
• Fang-Ming Tian
• Dun-Wei Gong
• Witold Pedrycz

As environmental problems are increasingly challenging and sustainable development win support among the people, the energy-efficient hybrid flow-shop scheduling problem (HFSP), as a scheduling problem with great application value, has been widely concerned. However, most existing research has focused on deterministic cases and uncertainty is rarely considered in energy-efficient HFSP (EHFSP), especially with various machine speed constraints. Uncertainty is often caused by some uncontrollable factors, such as human factors and ignoring uncertainty will greatly reduce the application value of the problem solutions. In this study, an energy-efficient fuzzy HFSP (EFHFSP) at a variable machine speed is considered and the existing non-dominated sorting genetic algorithm-II (NSGA-II) is extended to minimize fuzzy make-span and total fuzzy energy consumption simultaneously. The computation of total fuzzy energy consumption is given and reverse learning is proposed to produce the initial population. ENSGA-II adopts an effective genetic operator and its parameters ( P c and P m ) are adjustive. A novel strategy based on history information is also used to produce high-quality solutions. Extensive experiments are conducted to test the performance of ENSGA-II. ENSGA-II can provide promising results for EFHFSP.

EAAI Journal 2022 Journal Article

Information Quality for Intuitionistic Fuzzy Values with Its Application in Decision Making

• Dawei Xie
• Fuyuan Xiao
• Witold Pedrycz

Information quality has attracted increasing attention in recent years. In this paper, we propose a measure to quantify the information quality of intuitionistic fuzzy information based on a pseudo probability transformation, and derive its induced order to rank intuitionistic fuzzy values. The proposed measure and the induced order are proved to possess some well-defined properties that ensure rationality. Further, in order to rank intuitionistic fuzzy alternatives in decision making, we turn the proposed information quality into a ranking-oriented information quality, and present its induced order to generate the ranking. The decision capacity of our method is founded with the goal of rationalizing, simulating and further facilitating the interpretability and transparency in human decision making. A comparative study has been conducted, demonstrating the effectiveness of the proposed approach to ranking alternatives. The proposed approach is shown to outperform existing ranking methods via case-by-case comparisons. Finally, an application involving decision making of drug selection and an application in multi-criteria decision analysis of supplier selection are provided.

EAAI Journal 2021 Journal Article

Failure mode and effect analysis: A three-way decision approach

• Jiang-Hong Zhu
• Zhen-Song Chen
• Bin Shuai
• Witold Pedrycz
• Kwai-Sang Chin
• Luis Martínez

Failure mode and effect analysis (FMEA), as a powerful and effective risk assessment tool in reliability and safety analysis, has been extensively applied in different fields to enhance the reliability of a system. However, the traditional FMEA method has exposed some significant defects in practical applications. Furthermore, the risk ranking of failure modes is not convenient for subsequent maintenance strategies, and the interactions between risk criteria are not considered in most multiple criteria decision making (MCDM)-based methods. In this paper, we construct a novel FMEA model based on criteria importance through the inter-criteria correlation (CRITIC) method and three-way decisions to improve the performance of the conventional FMEA method and aid making a maintenance plan. Firstly, we introduce the decision-theoretic rough sets (DTRSs) into an interval 2-tuple linguistic (I2TL) environment to define interval 2-tuple linguistic decision-theoretic rough sets (I2TLDTRSs). Secondly, the CRITIC method is employed to calculate the weight of risk criteria. Thirdly, the conditional probability is obtained by a similarity-based method with the help of the ideas of the TOPSIS approach, and a relative loss function is designed by introducing risk avoidance coefficient. Fourthly, the classification of failure modes is derived according to the three-way decision rules. Finally, two numerical examples of the train door system are given to verify the effectiveness and practicality of the presented model. The presented model both extends the theory and application of three-way decisions and provides a solution beneficial for making a maintenance strategy to reduce the risk of failure modes.

EAAI Journal 2021 Journal Article

Interactive algorithms for normalized probabilistic linguistic preference relations in view of the disjunctive probability based consistency and consensus analysis

• Fanyong Meng
• Witold Pedrycz
• Jie Tang
• Hamido Fujita

This paper investigates decision making with normalized probabilistic linguistic preference relations (NPLPRs). Consistency analysis is indispensable for deriving the reasonable ranking. After recalling previous research, we find that all previous concepts cannot fully define consistent NPLPRs. As a fundamental topic of decision making with preference relations, it is necessary to further study the consistency of NPLPRs. For this purpose, an interactive algorithm for deriving disjunctive probabilistic additive linguistic preference relations (DP-ALPRs) is provided, by which an additive consistency concept for NPLPRs is defined. When NPLPRs are unacceptably consistent, models for obtaining acceptably additively consistent NPLPRs are built. Considering the situation where only incomplete NPLPRs are obtained, a disjunctive probability and additive consistency based interactive algorithm for ascertaining missing judgments is provided. Meanwhile, we discuss group decision making (GDM) with NPLPRs and offer a distance measure based formula to determine the weights of the decision makers. In addition, the method defines a consensus index and builds models for improving the consensus level. Under the additive consistency and consensus discussions, an interactive algorithm for GDM with NPLPRs is proposed. Finally, the new method is applied to select green raw material suppliers to illustrate the application and compared with several previous ones.

EAAI Journal 2021 Journal Article

Prediction model of burn-through point with fuzzy time series for iron ore sintering process

• Sheng Du
• Min Wu
• Luefeng Chen
• Witold Pedrycz

Burn-through point (BTP) is an essential parameter in the iron ore sintering process. Operators usually judge whether the current production is stable by monitoring the BTP. It comes with significant application prospects to predict the BTP accurately. A prediction model of the BTP with fuzzy time series is designed in this paper. First, the fuzzy time series prediction method with the Fuzzy C-Means clustering is presented as the core modeling method. A prediction model of the response is constructed to obtain a timely response to the current BTP. The prediction model of the difference is established to estimate the present unmeasurable disturbance on the BTP. Then, a hybrid prediction model is built, which realizes the composition of these two models by an adjustment factor. Finally, a series of experiments is carried out using the raw time series data from an iron and steel plant. The experimental result shows that the designed model has better prediction performance for the BTP than existing models, which is an advantage resulting from the hybrid structure and the fuzzy time series prediction model with the Fuzzy C-Means clustering. This prediction model of the BTP implies the foundation for the stable control of the iron ore sintering process.

TIST Journal 2021 Journal Article

RHUPS

• Yoonji Baek
• Unil Yun
• Heonho Kim
• Hyoju Nam
• Hyunsoo Kim
• Jerry Chun-Wei Lin
• Bay Vo
• Witold Pedrycz

Databases that deal with the real world have various characteristics. New data is continuously inserted over time without limiting the length of the database, and a variety of information about the items constituting the database is contained. Recently generated data has a greater influence than the previously generated data. These are called the time-sensitive non-binary stream databases, and they include databases such as web-server click data, market sales data, data from sensor networks, and network traffic measurement. Many high utility pattern mining and stream pattern mining methods have been proposed so far. However, they have a limitation that they are not suitable to analyze these databases, because they find valid patterns by analyzing a database with only some of the features described above. Therefore, knowledge-based software about how to find meaningful information efficiently by analyzing databases with these characteristics is required. In this article, we propose an intelligent information system that calculates the influence of the insertion time of each batch in a large-scale stream database by applying the sliding window model and mines recent high utility patterns without generating candidate patterns. In addition, a novel list-based data structure is suggested for a fast and efficient management of the time-sensitive stream databases. Moreover, our technique is compared with state-of-the-art algorithms through various experiments using real datasets and synthetic datasets. The experimental results show that our approach outperforms the previously proposed methods in terms of runtime, memory usage, and scalability.

EAAI Journal 2020 Journal Article

Bid evaluation in civil construction under uncertainty: A two-stage LSP-ELECTRE III-based approach

• Zhen-Song Chen
• Xuan Zhang
• Witold Pedrycz
• Xian-Jia Wang
• Miroslaw J. Skibniewski

Increasing growth and development of building industry and civil engineering motivates enterprises to advance bid evaluation process in an effort to ameliorate building quality and diminish construction cost. The selection of qualified construction contractor has been generally formulated and addressed as a multi-attribute group decision-making problem. However, few existing studies have taken the reasoning logic pattern in human cognition process and the compensatory among attributes into account. Therefore, a two-stage logic scoring of preference-ELECTRE III-based approach is established for the management and manipulation of bidder selection. In a bid to facilitate expert evaluation articulation, alternative-attribute assessments in this approach take the form of generalized comparative linguistic expressions, which are subsequently converted into possibility-distribution-based hesitant fuzzy linguistic term set via the similarity measure-based generation approach to enhance information quality and reliability. Then, nonlinear optimization-based direction rules are introduced to advance the automatic minimum-distance-based consensus reaching model and based on which, the individual semantic-embedded assessments are algorithmically adjusted to reach acceptable consensus. Furthermore, the expert and attribute importance degrees are extracted from both semantic and formal logic aspects, and the modified individual assessments are gathered by integrating the two-stage logic scoring of preference aggregation structure and the ELECTRE III method. The present approach provides a solution to bid evaluation problems considering the reasoning logic pattern in human cognition process and the compensatory among attributes. A practical case study and a comparative analysis are performed to demonstrate the feasibility and effectiveness of the established multi-attribute group decision-making approach.

EAAI Journal 2018 Journal Article

Development of information granules of higher type and their applications to granular models of time series

• Shuai Liu
• Witold Pedrycz
• Adam Gacek
• Yaping Dai

The study is devoted to the design of information granules of higher type (especially type-2) with the use of the principle of justifiable granularity. The development of granules is realized in two key phases: first, information granules of type-1 are formed and then they are extended to type-2 constructs. Following the principle, information granules are designed by establishing a sound balance between their experimental justification (legitimacy) and specificity (associated with their underlying semantics). The definitions of coverage and specificity of type-2 information granules are revised to capture the essence of these constructs. Detailed formulas are derived for several main categories of membership functions (namely, triangular, parabolic, and square root) as well as intervals. The study delivers detailed results for interval-valued fuzzy sets described by membership functions coming from the main classes listed above. Illustrative studies include synthetic data exhibiting some probabilistic properties. The direct application of information granules of type-1 and type-2 is demonstrated in the description and prediction of time series realized in the setting of information granules (with the resulting models referred to as granular models of time series).

EAAI Journal 2017 Journal Article

Efficient frequent itemsets mining through sampling and information granulation

• Zhongjie Zhang
• Witold Pedrycz
• Jian Huang

In this study, we propose an algorithm forming high quality approximate frequent itemsets from those datasets with a large scale of transactions. The results produced by the algorithm with high probability contain all frequent itemsets, no itemset with support much lower than the minimum support is included, and supports obtained by the algorithm are close to the real values. To avoid an over-estimated sample size and a significant computing overhead, the task of reducing data is decomposed into three subproblems, and sampling and information granulation are used to solve them one by one. Firstly, the algorithm obtains rough support of every item by sampling and removes those infrequent items, so the data are simplified. Then, another sample is taken from the simplified data, and is clustered into some information granules. After data reduction, these granules obtained in this way are mined by the improved Apriori. A tight guarantee for the quality of final results is provided. The performance of the approach is quantified through a series of experiments.

EAAI Journal 2015 Journal Article

Contrastive divergence for memristor-based restricted Boltzmann machine

• Ahmad Muqeem Sheri
• Aasim Rafique
• Witold Pedrycz
• Moongu Jeon

Restricted Boltzmann machines and deep belief networks have been shown to perform effectively in many applications such as supervised and unsupervised learning, dimensionality reduction and feature learning. Implementing networks, which use contrastive divergence as the learning algorithm on neuromorphic hardware, can be beneficial for real-time hardware interfacing, power efficient hardware and scalability. Neuromorphic hardware which uses memristors as synapses is one of the most promising areas to achieve the above-mentioned goals. This paper presents a restricted Boltzmann machine which uses a two memristor model to emulate synaptic weights and achieves learning using contrastive divergence.

AIIM Journal 2015 Journal Article

Corrigendum to “A characterization of electrocardiogram signals through optimal allocation of information granularity” [Artif. Intell. Med. 54 (2012) 125–134]

• Adam Gacek
• Witold Pedrycz

EAAI Journal 2015 Journal Article

Fuzzy clustering of time series data using dynamic time warping distance

• Hesam Izakian
• Witold Pedrycz
• Iqbal Jamal

Clustering is a powerful vehicle to reveal and visualize structure of data. When dealing with time series, selecting a suitable measure to evaluate the similarities/dissimilarities within the data becomes necessary and subsequently it exhibits a significant impact on the results of clustering. This selection should be based upon the nature of time series and the application itself. When grouping time series based on their shape information is of interest (shape-based clustering), using a Dynamic Time Warping (DTW) distance is a desirable choice. Using stretching or compressing segments of temporal data, DTW determines an optimal match between any two time series. In this way, time series exhibiting similar patterns occurring at different time periods, are considered as being similar. Although DTW is a suitable choice for comparing data with respect to their shape information, calculating the average of a collection of time series (which is required in clustering methods) based on this distance becomes a challenging problem. As the result, employing clustering techniques like K-Means and Fuzzy C-Means (where the cluster centers – prototypes are calculated through averaging the data) along with the DTW distance is a challenging task and may produce unsatisfactory results. In this study, three alternatives for fuzzy clustering of time series using DTW distance are proposed. In the first method, a DTW-based averaging technique proposed in the literature, has been applied to the Fuzzy C-Means clustering. The second method considers a Fuzzy C-Medoids clustering, while the third alternative comes as a hybrid technique, which exploits the advantages of both the Fuzzy C-Means and Fuzzy C-Medoids when clustering time series. Experimental studies are reported over a set of time series coming from the UCR time series database.

EAAI Journal 2015 Journal Article

Time series long-term forecasting model based on information granules and fuzzy clustering

• Weina Wang
• Witold Pedrycz
• Xiaodong Liu

In spite of the impressive diversity of models of time series, there is still an acute need to develop constructs that are both accurate and transparent. Meanwhile, long-term time series prediction is challenging and of great interest to both practitioners and research community. The role of information granulation is to organize detailed numerical data into some meaningful, semantically sound entities. With this regard, the design of time series forecasting models used the information granulation is interpretable and easily comprehended by humans. In order to cluster information granules, a modified fuzzy c-means which does not require that data have the same dimensionality is proposed. Then, we develop forecasting model combining the modified fuzzy c-means and information granulation for solving the problem of time series long-term prediction. Synthetic time series, chaotic Mackey–Glass time series, power demand, daily temperatures, stock index, and wind speed are used in a series of experiments. The experimental results show that the proposed model produces better forecasting results than several existing models.

AIIM Journal 2012 Journal Article

A characterization of electrocardiogram signals through optimal allocation of information granularity

• Adam Gacek
• Witold Pedrycz

Objective We propose and develop a concept of a granular representation of a collection of signals (patterns) where a prototype (representative) of such numeric signals is formed as a certain information granule (say, a set, fuzzy set, rough set, and alike) instead of a single numeric entity. As being more abstract, the granular format of the representative of the family of signals is more in rapport with the nature of the representation task itself. It is instrumental in quantifying the diversity of data and capture their inherent distribution characteristics. Methods and materials In the realization of the granular representation of the signals, we introduce a certain level of granularity (supplied in advance), which in the construction of the granular representative is regarded as an essential important modeling asset. A two-phase design is developed whose ultimate goal is to optimally allocate (distribute) the predefined level of granularity to the individual elements of the universe of discourse over which the signals are described. Given the nature of the required optimization, the ensuing optimization problem is solved by engaging a machinery of population-based optimization, namely Particle Swarm Optimization (PSO). Furthermore a number of information granularity distribution protocols are proposed. The numerical experiments completed for synthetic data and ECG MIT-BIH database signals are used to demonstrate the performance of the overall optimization algorithm and quantify the effectiveness of the allocation of information granularity realized by the PSO. An area under curve (AUC) criterion is proposed as a measure to express the quality of the overall optimization framework. Results For both synthetic as well as ECG signals, it is shown that the method endowed with the PSO identifies the best prototype and spans the lower and upper bounds of its granular counterpart. In addition to the numeric quantification of the best (optimized) granular prototype, the method helps visualizing its bounds. The relative difference in mapping performance between the best and the weakest granular prototypes is in the range of 18% (for normal ECG complexes) and over 26% in case of complexes of premature ventricular contraction. Conclusions A complete algorithm of the construction of granular prototypes is presented. Treating the granular prototype as a template of a given class of electrocardiogram (ECG) signals, a matching process is facilitated and used as a basis for the design of signal classification algorithms. Various realizations of granular prototypes can be completed with the use of fuzzy sets or rough sets.

EAAI Journal 2012 Journal Article

Design of optimized cascade fuzzy controller based on differential evolution: Simulation studies and practical insights

• Sung-Kwun Oh
• Wook-Dong Kim
• Witold Pedrycz

In this study, we discuss a design of an optimized cascade fuzzy controller for the rotary inverted pendulum system and ball & beam system by using an optimization vehicle of differential evolution (DE). The structure of the differential evolution optimization environment is simple and a convergence to optimal values realized here is very good in comparison to the convergence reported for other optimization algorithms. DE is easy to use given its mathematical operators. It also requires a limited computing overhead. The rotary inverted pendulum system and ball & beam system are nonlinear systems, which exhibit unstable motion. The performance of the proposed fuzzy controller is evaluated from the viewpoint of several performance criteria such as overshoot, steady-state error, and settling time. Their values are obtained through simulation studies and practical, real-world experiments. We evaluate and analyze the performance of the proposed optimal fuzzy controller optimized by Genetic Algorithm (GA), and DE. In this setting, we show the superiority of DE versus other methods being used here as well as highlight the characteristics of this optimization tool.

EAAI Journal 2010 Journal Article

Data compactification and computing with words

• Witold Pedrycz
• Stuart H. Rubin

The underlying objective of this study is to show how fuzzy sets (and information granules in general) and grammatical inference play an interdependent role in information granularization and knowledge-based problem characterization. The bottom-up organization of the material starts with a concept and selected techniques of data compactification which involves information granulation and gives rise to higher-order constructs (type-2 fuzzy sets). The detailed algorithmic investigations are provided. In the sequel, we focus on Computing with Words (CW), which in this context is treated as a general paradigm of processing information granules. We elaborate on a role of randomization and offer a detailed example illustrating the essence of the granular constructs along with the grammatical aspects of the processing.

AIJ Journal 2010 Journal Article

Positive approximation: An accelerator for attribute reduction in rough set theory

• Yuhua Qian
• Jiye Liang
• Witold Pedrycz
• Chuangyin Dang

Feature selection is a challenging problem in areas such as pattern recognition, machine learning and data mining. Considering a consistency measure introduced in rough set theory, the problem of feature selection, also called attribute reduction, aims to retain the discriminatory power of original features. Many heuristic attribute reduction algorithms have been proposed however, quite often, these methods are computationally time-consuming. To overcome this shortcoming, we introduce a theoretic framework based on rough set theory, called positive approximation, which can be used to accelerate a heuristic process of attribute reduction. Based on the proposed accelerator, a general attribute reduction algorithm is designed. Through the use of the accelerator, several representative heuristic attribute reduction algorithms in rough set theory have been enhanced. Note that each of the modified algorithms can choose the same attribute reduct as its original version, and hence possesses the same classification accuracy. Experiments show that these modified algorithms outperform their original counterparts. It is worth noting that the performance of the modified algorithms becomes more visible when dealing with larger data sets.

EAAI Journal 2009 Journal Article

The design of a fuzzy cascade controller for ball and beam system: A study in optimization with the use of parallel genetic algorithms

• Sung-Kwun Oh
• Han-Jong Jang
• Witold Pedrycz

In this study, we introduce a design methodology for an optimized fuzzy cascade controller for ball and beam system by exploiting the use of hierarchical fair competition-based genetic algorithm (HFCGA). The ball and beam system is a well-known control engineering experimental setup which consists of servo motor, beam and ball and exhibits a number of interesting and challenging properties when considered from the control perspective. The position of ball is determined through the control of a servo motor. The displacement change of the position of ball requires the change of the angle of the beam which determines the position angle of a servo motor. Consequently, the variation of the position of the moving ball and the ensuing change of the angle of the beam results in the change of the position angle of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer (1st) controller and the inner (2nd) controller in a cascaded architecture. Auto-tuning of the parameters of the controller (viz. scaling factors) of each fuzzy controller is realized with the use of the HFCGA. The set-point value of the inner controller (the 2nd controller) corresponds to the position angle of a servo motor, and is given as a reference value which enters into the inner controller as the 2nd controller of the two cascaded controllers. HFCGA is a kind of a parallel genetic algorithm (PGA), which helps alleviate an effect of premature convergence being a potential shortcoming present in conventional genetic algorithms (GAs). A detailed comparative analysis carried out from the viewpoint of the performance and the design methodology, is provided for the fuzzy cascade controller and the conventional PD cascade controller whose design relied on the use of the serial genetic algorithms.

EAAI Journal 2007 Journal Article

Forming consensus in the networks of knowledge

• Witold Pedrycz
• Kaoru Hirota

Information granules, such as e. g. , fuzzy sets, capture essential knowledge about data and the key dependencies between them. Quite commonly, we may envision that information granules (fuzzy sets) have become a result of fuzzy clustering and therefore could be succinctly represented in the form of some fuzzy partition matrices. Interestingly, the same data set could be represented from various standpoints and this multifaceted view yields a collection of different partition matrices being reflective of the higher-order granular knowledge about the data. The levels of specificity of the clusters the data are organized into could be quite different—the larger the number of clusters, the more detailed insight into the structure of data becomes available. Given the granularity of the resulting constructs (rather than plain data themselves), one could view a collection of partition matrices as a certain type of a network of knowledge. Considering a variety of sources of knowledge encountered across the network, we are interested in forming consensus between them. In a nutshell, this leads to the construction of certain fuzzy partition matrices which “reconcile” the knowledge captured by the individual partition matrices. Given that the granularity of the sources of knowledge under consideration could vary quite substantially, we develop a unified optimization perspective by introducing fuzzy proximity matrices that are induced by the corresponding partition matrices. In the sequel, the optimization is realized on a basis of these proximity matrices. We offer a detailed algorithm and illustrate its performance using a series of numeric experiments.

AIIM Journal 2004 Journal Article

Genetic design of feature spaces for pattern classifiers

• Witold Pedrycz
• Arnon Breuer
• Nicolino J. Pizzi

Functional piecewise approximation seeks data representation that is compact, highly simplified and meaningful. This study presents a genetic algorithm (GA)-based approach for computing a piecewise polynomial representation of functions, with the focus being on piecewise linear approximation in an application of biomedical spectral data. The area of piecewise linear approximation has been researched in the past four decades approximately, and the method presented here is compared with another well-known approach. The expansion of this method to piecewise polynomial representation is shown to be straightforward. Finally, the application of this method as a feature extraction method for classification of a dataset of feature vectors, specifically biomedical spectra, is demonstrated.

EAAI Journal 2004 Journal Article

Logic-driven fuzzy modeling with fuzzy multiplexers

• Witold Pedrycz

We introduce a concept of a fuzzy multiplexer, discuss its use in the design of logic networks and elaborate on the role of such networks in fuzzy modeling. In essence, a fuzzy multiplexer, fMUX, acts as a fuzzy switch whose output is determined on a basis of the logic values of the information inputs being switched (selected) by the select input. Multiplexers are generic building modules in digital systems. We show that networks of fuzzy multiplexers can play a similar role in fuzzy modeling. The two general design methodologies are studied and contrasted. The first is based on gradient-based learning and helps carry out parametric optimization. The second approach exploits a global genetic optimization and supports a structural and parametric development of the network. This becomes especially attractive in case of multivariable systems where the number of system variables has to be reduced. Experimental studies are reported for synthetic Boolean data and continuous (multivalued) problems.

EAAI Journal 2004 Journal Article

Parameter estimation of fuzzy controller and its application to inverted pendulum

• Sung-Kwun Oh
• Witold Pedrycz
• Seok-Beom Rho
• Tae-Chon Ahn

In this paper, a new approach to estimate scaling factors of the fuzzy PID controller is presented. The performance of the fuzzy PID controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing (more specifically, a genetic algorithm) and estimation algorithm. The tuning of the scaling factors of the fuzzy PID controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy PID controller by means of three types of estimation algorithms such as HCM (Hard C-Means) clustering-based regression polynomial, neuro-fuzzy networks, and regression polynomials. Numerical studies are presented in detail along with a detailed comparative analysis.

AIIM Journal 2002 Journal Article

An interactive framework for an analysis of ECG signals

• Giovanni Bortolan
• Witold Pedrycz

In this study, we introduce and discuss a development of a highly interactive and user-friendly environment for an ECG signal analysis. The underlying neural architecture being a crux of this environment comes in the form of a self-organizing map. This map helps discover a structure in a set of ECG patterns and visualize a topology of the data. The role of the designer is to choose from some already visualized regions of the self-organizing map characterized by a significant level of data homogeneity and substantial difference from other regions. In the sequel, the regions are described by means of information granules—fuzzy sets that are essential in the characterization of the main relationships existing in the ECG data. The study introduces an original method of constructing membership functions that incorporates class membership as an important factor affecting changes in membership grades. The study includes a comprehensive descriptive modeling of highly dimensional ECG data.