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Xinxing Wu

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4 papers

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EAAI Journal 2023 Journal Article

A prospect theory-based MABAC algorithm with novel similarity measures and interactional operations for picture fuzzy sets and its applications

Tao Wang
Xinxing Wu
Harish Garg
Qian Liu
Guanrong Chen

Picture fuzzy set (PFS) is one the reliable tool to handle the uncertainties in the data as compared to the intuitionistic fuzzy set (IFS) or fuzzy set. PFS simultaneously handle the four degrees namely, membership, neutrality, non-membership, and refusal, and thus widely applicable to solve the real-life decision-making problems more accurately. Keeping their advantages, in this paper, we present some interactive operational laws for the picture fuzzy numbers (PFNs) to aggregate picture fuzzy information. Also, we state some new information measures namely picture fuzzy similarity measures (PFSimMs) based on fuzzy strict negations, which can overcome the various drawbacks of the existing PFSimMs. The various properties and their features are studied in detail to show their advantages. Finally, we develop a prospect theory-based multi-attributive border approximation area comparison (MABAC) method under picture fuzzy environment by using the proposed operational laws and PFSimMs to solve the decision-making problems. The applicability of the developed algorithm is explained through a numerical example and show its superiorities.

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IJCAI Conference 2022 Conference Paper

Stabilizing and Enhancing Link Prediction through Deepened Graph Auto-Encoders

Xinxing Wu
Qiang Cheng

Graph neural networks have been widely used for a variety of learning tasks. Link prediction is a relatively under-studied graph learning task, with current state-of-the-art models based on one- or two-layer shallow graph auto-encoder (GAE) architectures. In this paper, we overcome the limitation of current methods for link prediction of non-Euclidean network data, which can only use shallow GAEs and variational GAEs. Our proposed methods innovatively incorporate standard auto-encoders (AEs) into the architectures of GAEs to capitalize on the intimate coupling of node and edge information in complex network data. Empirically, extensive experiments on various datasets demonstrate the competitive performance of our proposed approach. Theoretically, we prove that our deep extensions can inclusively express multiple polynomial filters with different orders. The codes of this paper are available at https: //github. com/xinxingwu-uk/DGAE.

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NeurIPS Conference 2021 Conference Paper

Algorithmic stability and generalization of an unsupervised feature selection algorithm

Xinxing Wu
Qiang Cheng

Feature selection, as a vital dimension reduction technique, reduces data dimension by identifying an essential subset of input features, which can facilitate interpretable insights into learning and inference processes. Algorithmic stability is a key characteristic of an algorithm regarding its sensitivity to perturbations of input samples. In this paper, we propose an innovative unsupervised feature selection algorithm attaining this stability with provable guarantees. The architecture of our algorithm consists of a feature scorer and a feature selector. The scorer trains a neural network (NN) to globally score all the features, and the selector adopts a dependent sub-NN to locally evaluate the representation abilities for selecting features. Further, we present algorithmic stability analysis and show that our algorithm has a performance guarantee via a generalization error bound. Extensive experimental results on real-world datasets demonstrate superior generalization performance of our proposed algorithm to strong baseline methods. Also, the properties revealed by our theoretical analysis and the stability of our algorithm-selected features are empirically confirmed.

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AAAI Conference 2021 Conference Paper

Fractal Autoencoders for Feature Selection

Xinxing Wu
Qiang Cheng

Feature selection reduces the dimensionality of data by identifying a subset of the most informative features. In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE). It trains a neural network to pinpoint informative features for global exploring of representability and for local excavating of diversity. Architecturally, FAE extends autoencoders by adding a one-to-one scoring layer and a small sub-neural network for feature selection in an unsupervised fashion. With such a concise architecture, FAE achieves state-of-the-art performances; extensive experimental results on fourteen datasets, including very high-dimensional data, have demonstrated the superiority of FAE over existing contemporary methods for unsupervised feature selection. In particular, FAE exhibits substantial advantages on gene expression data exploration, reducing measurement cost by about 15% over the widely used L1000 landmark genes. Further, we show that the FAE framework is easily extensible with an application.

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