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学者姓名:郑庆华

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Tax Evasion Detection With FBNE-PU Algorithm Based on PnCGCN and PU Learning EI SCIE Scopus
期刊论文 | 2023 , 35 (1) , 931-944 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
SCOPUS Cited Count: 3
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Abstract :

Tax evasion is an illegal activity in which individuals or entities avoid paying their true tax liabilities. Efficient detection of tax evasion has always been a crucial issue for both governments and academic researchers. Recent research has proposed the use of machine learning technology to detect tax evasion and has shown good results in some specific areas. Regrettably, there are still two major obstacles to detect tax evasion. First, it is hard to extract powerful features because of the complexity of tax data. Second, due to the complicated process of tax auditing, labeled data are limited in practice. Such obstacles motivate the contributions of this work. In this paper, we propose a novel tax evasion detection framework named FBNE-PU (Fusion of the basic feature and network embedding with PU learning for tax evasion detection), a multistage method for detecting tax evasion in real-life scenarios. In this paper, we perform an in-depth analysis of the characteristics of the transaction network and propose a novel network embedding algorithm, the PnCGCN. It significantly improves detection performance by extracting powerful features from basic features and the tax-related transaction network. Moreover, we use nnPU (positive-unlabeled learning with non-negative risk estimator) to assign pseudo labels for unlabeled data. Finally, an MLP is trained as the decision function. Experiments on three real-world datasets demonstrate that our method significantly outperforms the comparison methods in the tax evasion detection task. Additionally, the source code and the experimental details have been made available at (https://github.com/PiggyGaGa/FBNE-PU).

Keyword :

graph convolutional network network embedding PU learning Tax evasion detection transaction network

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GB/T 7714 Gao, Yuda , Shi, Bin , Dong, Bo et al. Tax Evasion Detection With FBNE-PU Algorithm Based on PnCGCN and PU Learning [J]. | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2023 , 35 (1) : 931-944 .
MLA Gao, Yuda et al. "Tax Evasion Detection With FBNE-PU Algorithm Based on PnCGCN and PU Learning" . | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35 . 1 (2023) : 931-944 .
APA Gao, Yuda , Shi, Bin , Dong, Bo , Wang, Yiyang , Mi, Lingyun , Zheng, Qinghua . Tax Evasion Detection With FBNE-PU Algorithm Based on PnCGCN and PU Learning . | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2023 , 35 (1) , 931-944 .
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Biased Complementary-Label Learning Without True Labels SCIE
期刊论文 | 2022 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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Abstract :

In complementary-label learning (CLL), the complementary transition matrix, denoting the probabilities that true labels flip into complementary labels (CLs) which specify classes observations do not belong to, is crucial to building statistically consistent classifiers. Most existing works implicitly assume that the transition probabilities are identical, which is not true in practice and may lead to undesirable bias in solutions. Few recent works have extended the problem to a biased setting but limit their explorations to modeling the transition matrix by exploiting the complementary class posteriors of anchor points (i.e., instances that almost certainly belong to a specific class). However, due to the severe corruption and unevenness of biased CLs, both anchor points and complementary class posteriors are difficult to predict accurately in the absence of true labels. In this article, rather than directly predicting these two error-prone items, we instead propose a divided-T estimator as an alternative to effectively learn transition matrices from only biased CLs. Specifically, we exploit semantic clustering to mitigate the adverse effects arising from CLs. By introducing the learned semantic clusters as an intermediate class, we factorize the original transition matrix into the product of two easy-to-estimate matrices that are not reliant on the two error-prone items. Both theoretical analyses and empirical results justify the effectiveness of the divided-T estimator for estimating transition matrices under a mild assumption. Experimental results on benchmark datasets further demonstrate that the divided-T estimator outperforms state-of-the-art (SOTA) methods by a substantial margin.

Keyword :

Biased complementary labels (CLs) Estimation error multiclass classification Research and development Robustness Self-supervised learning semantic clustering Semantics Training Training data transition matrix

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GB/T 7714 Ruan, Jianfei , Zheng, Qinghua , Zhao, Rui et al. Biased Complementary-Label Learning Without True Labels [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2022 .
MLA Ruan, Jianfei et al. "Biased Complementary-Label Learning Without True Labels" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022) .
APA Ruan, Jianfei , Zheng, Qinghua , Zhao, Rui , Dong, Bo . Biased Complementary-Label Learning Without True Labels . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2022 .
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Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling Scopus
期刊论文 | 2022 , 7 (1) , 16-29 | DATA SCIENCE AND ENGINEERING
SCOPUS Cited Count: 4
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Abstract :

Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs). It is a pivotal step for integrating KGs to increase knowledge coverage and quality. Recent years have witnessed a rapid increase of EA frameworks. However, state-of-the-art solutions tend to rely on labeled data for model training. Additionally, they work under the closed-domain setting and cannot deal with entities that are unmatchable. To address these deficiencies, we offer an unsupervised framework UEA that performs entity alignment in the open world. Specifically, we first mine useful features from the side information of KGs. Then, we devise an unmatchable entity prediction module to filter out unmatchable entities and produce preliminary alignment results. These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment. Finally, the progressive learning framework gradually improves the quality of structural embeddings and enhances the alignment performance. Furthermore, noticing that the pseudo-labeled data are of various qualities, we introduce the concept of confidence to measure the probability of an entity pair of being true and develop a confidence-based unsupervised EA framework CUEA. Our solutions do not require labeled data and can effectively filter out unmatchable entities. Comprehensive experimental evaluations validate the superiority of our proposals .

Keyword :

Entity alignment Knowledge graph Unsupervised learning

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GB/T 7714 Zhao, Xiang , Zeng, Weixin , Tang, Jiuyang et al. Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling [J]. | DATA SCIENCE AND ENGINEERING , 2022 , 7 (1) : 16-29 .
MLA Zhao, Xiang et al. "Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling" . | DATA SCIENCE AND ENGINEERING 7 . 1 (2022) : 16-29 .
APA Zhao, Xiang , Zeng, Weixin , Tang, Jiuyang , Li, Xinyi , Luo, Minnan , Zheng, Qinghua . Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling . | DATA SCIENCE AND ENGINEERING , 2022 , 7 (1) , 16-29 .
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A prediction model of student performance based on self-attention mechanism SCIE Scopus
期刊论文 | 2022 , 65 (2) , 733-758 | KNOWLEDGE AND INFORMATION SYSTEMS
SCOPUS Cited Count: 5
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Abstract :

Performance prediction is an important research facet of educational data mining. Most models extract student behavior features from campus card data for prediction. However, most of these methods have coarse time granularity, difficulty in extracting useful high-order behavior combination features, dependence on 6 historical achievements, etc. To solve these problems, this paper utilizes prediction of grade point average (GPA prediction) and whether a specific student has failing subjects (failing prediction) in a term as the goal of performance prediction and proposes a comprehensive performance prediction model of college students based on behavior features. First, a method for representing campus card data based on behavior flow is introduced to retain higher time accuracy. Second, a method for extracting student behavior features based on multi-head self-attention mechanism is proposed to automatically select more important high-order behavior combination features. Finally, a performance prediction model based on student behavior feature mode difference is proposed to improve the model's prediction accuracy and increases the model's robustness for students with significant changes in performance. The performance of the model is verified on actual data collected by the teaching monitoring big data platform of Xi'an Jiaotong University. The results show that the model's prediction performance is better than the comparison algorithms on both the failing prediction and GPA prediction.

Keyword :

Failing prediction GPA prediction Mode difference Performance prediction Self-attention mechanism

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GB/T 7714 Chen, Yan , Wei, Ganglin , Liu, Jiaxin et al. A prediction model of student performance based on self-attention mechanism [J]. | KNOWLEDGE AND INFORMATION SYSTEMS , 2022 , 65 (2) : 733-758 .
MLA Chen, Yan et al. "A prediction model of student performance based on self-attention mechanism" . | KNOWLEDGE AND INFORMATION SYSTEMS 65 . 2 (2022) : 733-758 .
APA Chen, Yan , Wei, Ganglin , Liu, Jiaxin , Chen, Yunwei , Zheng, Qinghua , Tian, Feng et al. A prediction model of student performance based on self-attention mechanism . | KNOWLEDGE AND INFORMATION SYSTEMS , 2022 , 65 (2) , 733-758 .
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ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning EI SCIE Scopus
期刊论文 | 2022 , 44 (12) , 9733-9740 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
SCOPUS Cited Count: 96
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Abstract :

In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good performance across diversified datasets or scenarios. Accordingly, in this paper, we turn to neural architecture search (NAS) and make the first attempt to bring NAS techniques into the ZSL realm. Specifically, we propose a differentiable GAN architecture search method over a specifically designed search space for zero-shot learning, referred to as ZeroNAS. Considering the relevance and balance of the generator and discriminator, ZeroNAS jointly searches their architectures in a min-max player game via adversarial training. Extensive experiments conducted on four widely used benchmark datasets demonstrate that ZeroNAS is capable of discovering desirable architectures that perform favorably against state-of-the-art ZSL and generalized zero-shot learning (GZSL) approaches. Source code is at https://github.com/caixiay/ZeroNAS.

Keyword :

Computer architecture Differentiable architecture search generative adversarial networks Generative adversarial networks Generators Optimization Task analysis Testing Training zero-shot learning

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GB/T 7714 Yan, Caixia , Chang, Xiaojun , Li, Zhihui et al. ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning [J]. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2022 , 44 (12) : 9733-9740 .
MLA Yan, Caixia et al. "ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning" . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 44 . 12 (2022) : 9733-9740 .
APA Yan, Caixia , Chang, Xiaojun , Li, Zhihui , Guan, Weili , Ge, Zongyuan , Zhu, Lei et al. ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2022 , 44 (12) , 9733-9740 .
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Aspect-Based Sentiment Analysis With Heterogeneous Graph Neural Network EI SCIE Scopus
期刊论文 | 2022 | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
WoS CC Cited Count: 1 SCOPUS Cited Count: 16
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Abstract :

Aspect-based sentiment analysis aims to predict sentiment polarities of given aspects in text. Most current approaches employ attention-based neural methods to capture semantic relationships between aspects and words in one sentence. However, these methods ignore the fact that sentences with the same aspect and sentiment polarity often share the structure and semantic information in a domain, which leads to lower model performance. To mitigate this problem, we propose a heterogeneous aspect graph neural network (HAGNN) to learn the structure and semantic knowledge from intersentence relationships. Our model is a heterogeneous graph neural network since it contains three different kinds of nodes: word nodes, aspect nodes, and sentence nodes. These nodes can pass structure and semantic information between each other and update their embeddings to improve the performance of our model. To the best of our knowledge, we are the first to use a heterogeneous graph to capture relationships between sentences and aspects. The experimental results on five public datasets show the effectiveness of our model outperforming some state-of-the-art models.

Keyword :

Aspect-based sentiment analysis (ABSA) aspect-category sentiment analysis (ACSA) Computational modeling Data mining Graph neural networks heterogeneous graph neural network (GNN) Predictive models Semantics Sentiment analysis Task analysis

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GB/T 7714 An, Wenbin , Tian, Feng , Chen, Ping et al. Aspect-Based Sentiment Analysis With Heterogeneous Graph Neural Network [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2022 .
MLA An, Wenbin et al. "Aspect-Based Sentiment Analysis With Heterogeneous Graph Neural Network" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2022) .
APA An, Wenbin , Tian, Feng , Chen, Ping , Zheng, Qinghua . Aspect-Based Sentiment Analysis With Heterogeneous Graph Neural Network . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2022 .
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Adaptive Video Streaming Using Dynamic Server Push over HTTP/2 EI CPCI-S
会议论文 | 2021 , 673-678 | 24th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021
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Abstract :

With the increasing popularity of video services, HTTP adaptive streaming (HAS) has become the mainstream technology for media streaming distribution. In traditional HAS over HTTP/1.1, the HAS server responds to each request from the client individually. This process adds additional round-trip time, resulting in underestimation of available bandwidth. As a result, the HAS client chooses a lower bitrate, which reduces network utilization and the user's quality of experience. In recent years, the HTTP/2 protocol has emerged, which allows server to actively push multiple data segments to the client. Pushing multiple segments can reduce the negative impact of network latency on estimating available bandwidth, thereby increasing the user's request bitrate and video quality. However, when the network is unstable, the more video segments that are pushed by the server, the more challenges the client encounters in responding to network fluctuations i n time, causing playback stalling and poor user experience. Therefore, this paper proposes a dynamic server push algorithm over HTTP/2, which chooses a different number of segments for server push according to network fluctuations. For the evaluation results, relative to its benchmarks, the proposed approach improves the average video request bitrate while minimizing the probability of playback stalling. © 2021 IEEE.

Keyword :

Bandwidth HTTP Hypertext systems Interactive computer systems Quality of service User experience Video streaming

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GB/T 7714 Huang, Shouqin , Wang, Zhiwen , Zhang, Weizhan et al. Adaptive Video Streaming Using Dynamic Server Push over HTTP/2 [C] . 2021 : 673-678 .
MLA Huang, Shouqin et al. "Adaptive Video Streaming Using Dynamic Server Push over HTTP/2" . (2021) : 673-678 .
APA Huang, Shouqin , Wang, Zhiwen , Zhang, Weizhan , Du, Haipeng , Zheng, Qinghua . Adaptive Video Streaming Using Dynamic Server Push over HTTP/2 . (2021) : 673-678 .
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Self-weighted Robust LDA for Multiclass Classification with Edge Classes SCIE
期刊论文 | 2021 , 12 (1) | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
WoS CC Cited Count: 21
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Abstract :

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are prone to be dominated by the class with very large deviation from the others, i.e., edge class, which occurs frequently in multi-class classification. First, the existence of edge classes often makes the total mean biased in the calculation of between-class scatter matrix. Second, the exploitation of l(2)-norm based between-class distance criterion magnifies the extremely large distance corresponding to edge class. In this regard, a novel self-weighted robust LDA with l(2,1)-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes. SWRLDA can automatically avoid the optimal mean calculation and simultaneously learn adaptive weights for each class pair without setting any additional parameter. An efficient re-weighted algorithm is exploited to derive the global optimum of the challenging l(2,1)-norm maximization problem. The proposed SWRLDA is easy to implement and converges fast in practice. Extensive experiments demonstrate that SWRLDA performs favorably against other compared methods on both synthetic and real-world datasets while presenting superior computational efficiency in comparison with other techniques.

Keyword :

dimension reduction edge class multi-class classification Robust linear discriminant analysis

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GB/T 7714 Yan, Caixia , Chang, Xiaojun , Luo, Minnan et al. Self-weighted Robust LDA for Multiclass Classification with Edge Classes [J]. | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY , 2021 , 12 (1) .
MLA Yan, Caixia et al. "Self-weighted Robust LDA for Multiclass Classification with Edge Classes" . | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 12 . 1 (2021) .
APA Yan, Caixia , Chang, Xiaojun , Luo, Minnan , Zheng, Qinghua , Zhang, Xiaoqin , Li, Zhihui et al. Self-weighted Robust LDA for Multiclass Classification with Edge Classes . | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY , 2021 , 12 (1) .
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A Novel Large Group Decision-Making Method via Normalized Alternative Prediction Selection EI SCIE
期刊论文 | 2021 , 29 (7) , 1992-2005 | IEEE TRANSACTIONS ON FUZZY SYSTEMS
WoS CC Cited Count: 2
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Abstract :

When a small portion of the decision makers hold the correct information and the majority hold the opposite, the correct ranking of the alternatives for the group decision-making cannot be obtained with the current methods. A novel method is thus developed to tackle this challenge in this article. The priori probabilities of each alternative can be calculated via the opinions of the group decision makers, which are presented as the pairwise comparisons of the alternatives in the form of the linguistic preference relation. Based on the aggregated probabilities of the alternatives in the group of the decision makers, the normalized-prediction selection rate (NPSR) is defined and calculated accordingly. The alternative with maximal NPSR is selected as the correct answer, whereas the accuracy of the correct alternative selection (CAS) is guaranteed by two propositions. The iterative algorithm is first devised to determine the ranking of the alternatives depending on the CAS. For the proposed method, the decision makers require no modification of the opinions as can avoid the consensus problem, and the CAS can be obtained under the circumstances that the correct information is held by the minority of the group. Finally, the experiment has been conducted to demonstrate the efficacy of the proposed method to obtain the CAS, and the main limitations of proposed method are carefully addressed as well.

Keyword :

Consensus measure Data analysis Decision making Finance group decision-making (GDM) Indexes Linguistics normalized-prediction selection rate (NPSR) Numerical models Probability ranking of alternatives

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GB/T 7714 Zhang, Hengshan , Zhou, Yimin , Zheng, Qinghua et al. A Novel Large Group Decision-Making Method via Normalized Alternative Prediction Selection [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2021 , 29 (7) : 1992-2005 .
MLA Zhang, Hengshan et al. "A Novel Large Group Decision-Making Method via Normalized Alternative Prediction Selection" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 29 . 7 (2021) : 1992-2005 .
APA Zhang, Hengshan , Zhou, Yimin , Zheng, Qinghua , Wang, Zhongmin , Chen, Yanping , Chen, Chunru et al. A Novel Large Group Decision-Making Method via Normalized Alternative Prediction Selection . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2021 , 29 (7) , 1992-2005 .
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Knowledge forest: a novel model to organize knowledge fragments EI SCIE CSCD
期刊论文 | 2021 , 64 (7) | Science China Information Sciences
WoS CC Cited Count: 2
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GB/T 7714 Zheng, Qinghua , Liu, Jun , Zeng, Hongwei et al. Knowledge forest: a novel model to organize knowledge fragments [J]. | Science China Information Sciences , 2021 , 64 (7) .
MLA Zheng, Qinghua et al. "Knowledge forest: a novel model to organize knowledge fragments" . | Science China Information Sciences 64 . 7 (2021) .
APA Zheng, Qinghua , Liu, Jun , Zeng, Hongwei , Guo, Zhaotong , Wu, Bei , Wei, Bifan . Knowledge forest: a novel model to organize knowledge fragments . | Science China Information Sciences , 2021 , 64 (7) .
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