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Zhigang Chen

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9 papers

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EAAI Journal 2024 Journal Article

Prediction of production indicators of fractured-vuggy reservoirs based on improved Graph Attention Network

Zhigang Chen
Dongmei Zhang
Jinping Li
Gang Hui
Rucheng Zhou

Various storage and seepage spaces exist in fractured-vuggy carbonate reservoirs composed of multi-scale dissolution pores and fractures. The frequent regulation of the working system causes nonlinear and unstable production data along with complex water breakthrough characteristics, which lead to difficulty in real-time prediction. Traditional methods based on the water drive curve and autoregressive machine learning ignore the spatial correlation among production wells and local geological characteristics. To address these problems, we propose a model based on improved attention, the spatiotemporal multi-graph convolutional network (STMCN), for production prediction. The unit production wells are abstracted as directed graph network nodes to establish adjacency, connectivity and correlation graphs to extract the spatial and semantic features from different perspectives through graph convolutional networks. To depict the law of fluid movement, we realize the fusion of spatial and semantic information through dynamic routing. Aiming at the autocorrelation characteristics of production sequences, the model uses the self-attention mechanism to capture the dependencies in production sequences. The gating mechanism is designed to achieve dynamic production prediction by adaptively aggregating the spatiotemporal characteristics. This paper evaluates the predictive performance of our model by two real-world datasets of the Tahe Oilfield. The results show that the mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) of our proposed model for the prediction of well A2 were 3. 19, 4. 66 and 0. 07, respectively, which were better than the relatively new baseline model.

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JBHI Journal 2022 Journal Article

JAN: Joint Attention Networks for Automatic ICD Coding

Yuzhou Wu
Zhigang Chen
Xin Yao
Xuechen Chen
Zeren Zhou
Jinkai Xue

The International Classification of Diseases (ICD) code is a disease classification method formulated by the World Health Organization(WHO). ICD coding usually requires clinicians to manually allocate ICD codes to clinical documents, which is labor-intensive, expensive, and error-prone. Therefore, many methods have been introduced for automatic ICD coding. However, most of the methods have ignored or cannot combine two essential features well: long-tailed label distribution and label correlation. In this paper, we propose a novel end-to-end Joint Attention Network (JAN) to solve these two problems. JAN includes Document-based attention and Label-based attention to capture semantic information from clinical document text and label description, respectively, which helps solve the classification of dense and sparse data in long-tailed label distribution. Besides, an Adaptive fusion layer and CorNet block are presented to adaptively adjust the weight of these two attentions and exploit label co-occurrence relations, respectively. Experiments on the MIMIC-III and MIMIC-II datasets demonstrate that our proposed JAN outperformed previous state-of-art methods achieving Micro-F1 of 0. 553, Micro-AUC of 0. 989 and precision at top 8(P@8) of 0. 735. Finally, we also provide attention and label correlation visualization to verify the effectiveness of our model and improve the interpretation of our deep learning-based method.

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JBHI Journal 2021 Journal Article

A Multiprocessing Scheme for PET Image Pre-Screening, Noise Reduction, Segmentation and Lesion Partitioning

Runxi Cui
Zhigang Chen
Jia Wu
YanLin Tan
GengHua Yu

Objective: Accurate segmentation and partitioning of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, staging and prognosis. Currently, PET segmentation and lesion partitioning are manually measured by radiologists, which is time consuming and laborious, and tedious manual procedures might lead to inaccurate measurement results. Therefore, we designed a new automatic multiprocessing scheme for PET image pre-screening, noise reduction, segmentation and lesion partitioning in this study. PET image pre-screening can reduce the time cost of noise reduction, segmentation and lesion partitioning methods, and denoising can enhance both quantitative metrics and visual quality for better segmentation accuracy. For pre-screening, we propose a new differential activation filter (DAF) to screen the lesion images from whole-body scanning. For noise reduction, neural network inverse (NN inverse) as the inverse transformation of generalized Anscombe transformation (GAT), which does not depend on the distribution of residual noise, was presented to improve the SNR of images. For segmentation and lesion partitioning, definition density peak clustering (DDPC) was proposed to realize instance segmentation of lesion and normal tissue with unsupervised images, which helped reduce the cost of density calculation and completely deleted the cluster halo. The experimental results of clinical data demonstrate that our proposed methods have good results and better performance in noise reduction, segmentation and lesion partitioning compared with state-of-the-art methods.

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AAAI Conference 2021 Conference Paper

Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training

Tao Chen
Haochen Shi
Liyuan Liu
Siliang Tang
Jian Shao
Zhigang Chen
Yueting Zhuang

With recent advances in distantly supervised (DS) relation extraction (RE), considerable attention is attracted to leverage multi-instance learning (MIL) to distill high-quality supervision from the noisy DS. Here, we go beyond label noise and identify the key bottleneck of DS-MIL to be its low data utilization: as high-quality supervision being refined by MIL, MIL abandons a large amount of training instances, which leads to a low data utilization and hinders model training from having abundant supervision. In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels. Specifically, since VAT is label-free, we employ the instance-level VAT to recycle instances abandoned by MIL. Besides, we deploy AT at the bag-level to unleash the full potential of the high-quality supervision got by MIL. Our proposed method brings consistent improvements (∼ 5 absolute AUC score) to the previous state of the art, which verifies the importance of the data utilization issue and the effectiveness of our method.

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IJCAI Conference 2020 Conference Paper

Alleviate Dataset Shift Problem in Fine-grained Entity Typing with Virtual Adversarial Training

Haochen Shi
Siliang Tang
Xiaotao Gu
Bo Chen
Zhigang Chen
Jian Shao
Xiang Ren

The recent success of Distant Supervision (DS) brings abundant labeled data for the task of fine-grained entity typing (FET) without human annotation. However, the heuristically generated labels inevitably bring a significant distribution gap, namely dataset shift, between the distantly labeled training set and the manually curated test set. Considerable efforts have been made to alleviate this problem from the label perspective by either intelligently denoising the training labels, or designing noise-aware loss functions. Despite their progress, the dataset shift can hardly be eliminated completely. In this work, complementary to the label perspective, we reconsider this problem from the model perspective: Can we learn a more robust typing model with the existence of dataset shift? To this end, we propose a novel regularization module based on virtual adversarial training (VAT). The proposed approach first uses a self-paced sample selection function to select suitable samples for VAT, then constructs virtual adversarial perturbations based on the selected samples, and finally regularizes the model to be robust to such perturbations. Experiments on two benchmarks demonstrate the effectiveness of the proposed method, with an average 3. 8%, 2. 5%, and 3. 2% improvement in accuracy, Macro F1 and Micro F1 respectively compared to the next best method.

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AAAI Conference 2019 Conference Paper

Cooperative Multimodal Approach to Depression Detection in Twitter

Tao Gui
Liang Zhu
Qi Zhang
Minlong Peng
Xu Zhou
Keyu Ding
Zhigang Chen

The advent of social media has presented a promising new opportunity for the early detection of depression. To do so effectively, there are two challenges to overcome. The first is that textual and visual information must be jointly considered to make accurate inferences about depression. The second challenge is that due to the variety of content types posted by users, it is difficult to extract many of the relevant indicator texts and images. In this work, we propose the use of a novel cooperative multi-agent model to address these challenges. From the historical posts of users, the proposed method can automatically select related indicator texts and images. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods by a large margin (over 30% error reduction). In several experiments and examples, we also verify that the selected posts can successfully indicate user depression, and our model can obtained a robust performance in realistic scenarios.

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AAAI Conference 2019 Conference Paper

Trainable Undersampling for Class-Imbalance Learning

Minlong Peng
Qi Zhang
Xiaoyu Xing
Tao Gui
Xuanjing Huang
Yu-Gang Jiang
Keyu Ding
Zhigang Chen

Undersampling has been widely used in the class-imbalance learning area. The main deficiency of most existing undersampling methods is that their data sampling strategies are heuristic-based and independent of the used classifier and evaluation metric. Thus, they may discard informative instances for the classifier during the data sampling. In this work, we propose a meta-learning method built on the undersampling to address this issue. The key idea of this method is to parametrize the data sampler and train it to optimize the classification performance over the evaluation metric. We solve the non-differentiable optimization problem for training the data sampler via reinforcement learning. By incorporating evaluation metric optimization into the data sampling process, the proposed method can learn which instance should be discarded for the given classifier and evaluation metric. In addition, as a data level operation, this method can be easily applied to arbitrary evaluation metric and classifier, including non-parametric ones (e. g. , C4. 5 and KNN). Experimental results on both synthetic and realistic datasets demonstrate the effectiveness of the proposed method.

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TIST Journal 2018 Journal Article

Fuzzy Cognitive Diagnosis for Modelling Examinee Performance

Qi Liu
Runze Wu
Enhong Chen
Guandong Xu
Yu Su
Zhigang Chen
Guoping Hu

Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people’s learning activities in educational settings. One of the key EDM tasks is cognitive modelling with examination data, and cognitive modelling tries to profile examinees by discovering their latent knowledge state and cognitive level (e.g. the proficiency of specific skills). However, to the best of our knowledge, the problem of extracting information from both objective and subjective examination problems to achieve more precise and interpretable cognitive analysis remains underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees’ cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then we combine fuzzy set theory and educational hypotheses to model the examinees’ mastery on the problems based on their skill proficiency. Finally, we simulate the generation of examination score on each problem by considering slip and guess factors. In this way, the whole diagnosis framework is built. For further comprehensive verification, we apply our FuzzyCDF to three classical cognitive assessment tasks, i.e., predicting examinee performance, slip and guess detection, and cognitive diagnosis visualization. Extensive experiments on three real-world datasets for these assessment tasks prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively.

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IJCAI Conference 2015 Conference Paper

Cognitive Modelling for Predicting Examinee Performance

Runze Wu
Qi Liu
Yuping Liu
Enhong Chen
Yu Su
Zhigang Chen
Guoping Hu

Cognitive modelling can discover the latent characteristics of examinees for predicting their performance (i. e. scores) on each problem. As cognitive modelling is important for numerous applications, e. g. personalized remedy recommendation, some solutions have been designed in the literature. However, the problem of extracting information from both objective and subjective problems to get more precise and interpretable cognitive analysis is still underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees’ cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then, we combine fuzzy set theory and educational hypotheses to model the examinees’ mastery on the problems. Further, we simulate the generation of examination scores by considering both slip and guess factors. Extensive experiments on three realworld datasets prove that FuzzyCDF can predict examinee performance more effectively, and the output of FuzzyCDF is also interpretative.

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