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Analysis on weighted AUC for imbalanced data learning through isometrics

Journal of computational Information Systems 2012年第1期8卷 371-378页

作者： Dong, Yuanfang Li, Xiongfei Li, Jun Zhao, Haiying Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 China School of Economics and Management Changchun University of Science and Technology Changchun 130022 China Department of Mathematics Changchun University of Science and Technology Changchun 130022 China School of Computer Science and Technology Xinjiang Normal University Wulumuqi 830000 China

A performance evaluation model, weighted AUC (wAUC), is proposed to determine a better way to measure the imbalanced data learning classifiers. When computing the weighted area under the ROC curve, weights vary with the values of the true positive rate (TPrate) among regions in a bid to focus on the accuracy of minority class that is more important in common. As AUC is a special case of wAUC, wAUC is compared with other common performance evaluation measures by isometric analysis. The experimental results show that wAUC can distinguish the classifiers with the same AUC values. © 2012 Binary Information Press January 2012.

关键词： Learning systems

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Using SROIQB to model and compose semantic Web service

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Tongxin Xuebao/Journal on Communications 2010年第8 A期31卷 1-9页

作者： Liu, Da-You Liu, Si-Pei Qi, Hong College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

One kind of semantic Web service modeling and reasoning method based on description logic with Boolean role constructors (SROIQB) was introduced. By using DL to figure out the hyponymy relationship between concepts in domain ontology and model Web service functional semantic in ServiceProfile, and define Boolean role expressions to describe the composition process in ProcessProfile respectively. Furthermore, Boolean role expression is introduced to describe all possible composition structures of atomic services and then semantic Web service composition can be regarded as a process of consistence checking and class subsumption computing reasoning on DL knowledge base. Thus the comparison and example validation show that introduced one kind of new and reasonable semantic Web service modeling and composition method in the framework of semantic Web by using SROIQB, which with more strong expressive capability and decidable type inference.

关键词： Semantic Web

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Deep Feature-Based Text Clustering and Its Explanation

Deep Feature-Based Text Clustering and Its Explanation

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International Conference on Data engineering

作者： Renchu Guan Hao Zhang Yanchun Liang Fausto Giunchiglia Lan Huang Xiaoyue Feng Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China DISI University of Trento Trento Italy

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. Our code is available at https://***/KEAML-JLU/DeepTextClustering.

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Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases

arXiv

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arXiv 2023年

作者： Li, Yingji Du, Mengnan Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

As the representation capability of Pre-trained Language Models (PLMs) improve, there is growing concern that they will inherit social biases from unprocessed corpora. Most previous debiasing techniques used Counterfactual Data Augmentation (CDA) to balance the training corpus. However, CDA slightly modifies the original corpus, limiting the representation distance between different demographic groups to a narrow range. As a result, the debiasing model easily fits the differences between counterfactual pairs, which affects its debiasing performance with limited text resources. In this paper, we propose an adversarial training-inspired two-stage debiasing model using Contrastive learning with Continuous Prompt Augmentation (named CCPA) to mitigate social biases in PLMs’ encoding. In the first stage, we propose a data augmentation method based on continuous prompt tuning to push farther the representation distance between sample pairs along different demographic groups. In the second stage, we utilize contrastive learning to pull closer the representation distance between the augmented sample pairs and then fine-tune PLMs’ parameters to get debiased encoding. Our approach guides the model to achieve stronger debiasing performance by adding difficulty to the training process. Extensive experiments show that CCPA outperforms baselines in terms of debiasing performance. Meanwhile, experimental results on the GLUE benchmark show that CCPA retains the language modeling capability of PLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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A non-genetic algorithm for solving multi-objective decision-making problems

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Journal of computational Information Systems 2012年第17期8卷 7401-7408页

作者： Li, Hengyan Chang, Shuizhen Xu, Zhonghai Mathematics and Information Science College North China University of Water Resources and Electric Power Zhengzhou 450011 China Mathematics and Information Science College Luoyang Normal University Luoyang 471022 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of Technology Harbin 150080 China

Multi-objective decision-making problems have been widely applied to business, financial investment, transportation, optimal routing design, environment protection, and military strategies. In this paper, we give a non-genetic algorithm for solving multi-objective decision-making problems. Under suitable conditions, a smooth path will be proven to exist. This can lead to an implementable globally convergent algorithm. We also give some numerical examples to illustrate the effectiveness of the algorithm in this paper. © 2012 Binary Information Press.

关键词： Genetic algorithms

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Searching for closed itemset with formal concept analysis

Searching for closed itemset with formal concept analysis

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hong Qi Da-You Liu Cheng-Quan Hu Ming Lu Liang Zhao Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Jilin University Changchun Jilin CN Coll. of Comput. Sci. & Technol. Jilin Univ. Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)0780384032

The problem of mining frequent patterns plays an essential role in many important data mining tasks. However, it often generates a very large number of frequent itemsets. The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. An algorithm Closearcher based on formal concept analysis for closed itemset searching is proposed. This algorithm divides the whole search space of closed itemsets into several subspaces in accordance with criterions prescribed ahead, and introduces an efficient scheme to recognize the valid ones, in which the search for closed itemsets is bounded. An intermediate structure is employed to judge the validity of a subspace and search closed itemsets more efficiently. The algorithm is experimental evaluated and compared with the famous NextClosure algorithm proposed by Ganter for random generated data, as well as for real application data. The results show that our algorithm performs much better than the later.

关键词： Itemsets Data mining Machine learning algorithms Pattern analysis Frequency Algorithm design and analysis Databases Educational institutions Computer science Educational technology

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A Multi-Task-Based Deep Multi-Scale Information Fusion Method for Intelligent Diagnosis of Bearing Faults

SSRN

引用

SSRN 2022年

作者： Xin, Ruihao Wang, Tiantian Miao, Fengbo Yu, Cuinan Feng, Xin College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin132000 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Science Jilin Institute of Chemical Technology Jilin Jilin132000 China

The use of deep learning for fault diagnosis is already a commonly used method. However, complex operating conditions under different working conditions significantly impact bearing vibration signals. Integrating the discriminative information of fault type and size into the deep learning model to achieve rich multi-task fault feature diagnosis still deserves attention. This study proposes a deep multi-task-based multi-scale feature fusion network MEAT(multi-task-based deep multi-scale feature fusion network) model to address these issues. Solve the limitations and poor adaptability of traditional convolutional neural network models under complex work. This model considers two sub-tasks, fault scale discrimination, and fault type discrimination, in the fault diagnosis multi-classification task through multi-task mapping decomposition. First, multi-dimensional features are extracted by convolution at different scales to obtain multi-dimensional fault information at different levels. Then these features are weighted and fused using a multi-layer attention mechanism to get accurate diagnosis results of bearing faults. © 2022, The Authors. All rights reserved.

关键词： Failure analysis

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Using TCM in mobile terminals in internet of vehicles

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Dongbei Daxue Xuebao/Journal of Northeastern University 2011年第SUPPL.1期32卷 107-110页

作者： Gao, Wen-Bin Liu, Yan-Heng Wang, Jian College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

A trusted cryptography module (TCM) is introduced into vehicular electronic systems to solve security issues of a mobile terminal in the Internet of vehicles, such as authentication, integrity assessment and so on. TCM has many security features, including integrity measure, integrity report and trusted storage, which can make a vehicular electronic system achieve a trusted boot process and encrypt data and information for storage protection, thereby improving the privacy and security of vehicular platform information. Referring to the TNC(trusted network connect)architecture, we design a kind of vehicular terminal access model compatible with the TNC to authenticate and authorize the trusted vehicular platform with TCM to access Internet of vehicles. The vehicular terminal access model proposed will improve the credibility and security of the Internet of vehicles.

关键词： Authentication

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RiWalk: Fast structural node embedding via role identification

arXiv

引用

arXiv 2019年

作者： Ma, Xuewei Qin, Geng Qiu, Zhiyang Zheng, Mingxin Wang, Zhe College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

Nodes performing different functions in a network have different roles, and these roles can be gleaned from the structure of the network. Learning latent representations for the roles of nodes helps to understand the network and to transfer knowledge across networks. However, most existing structural embedding approaches suffer from high computation and space cost or rely on heuristic feature engineering. Here we propose RiWalk, a flexible paradigm for learning structural node representations. It decouples the structural embedding problem into a role identification procedure and a network embedding procedure. Through role identification, rooted kernels with structural dependencies kept are built to better integrate network embedding methods. To demonstrate the effectiveness of RiWalk, we develop two different role identification methods named RiWalk-SP and RiWalk-WL respectively and employ random walk based network embedding methods. Experiments on within-network classification tasks show that our proposed algorithms achieve comparable performance with other baselines while being an order of magnitude more efficient. Besides, we also conduct across-network role classification tasks. The results show potential of structural embeddings in transfer learning. RiWalk is also scalable, making it capable of capturing structural roles in massive networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Network embeddings

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CellMirror: Deciphering Cell Populations from Spatial Transcriptomics Data by Interpretable Contrastive Learning 1

CellMirror: Deciphering Cell Populations from Spatial Transc...

引用

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xia, Junjie Cui, Jiangnan Huang, Zhenyu Zhang, Shuangquan Yao, Fang Zhang, Yijian Zuo, Chunman Institute of Artificial Intelligence Donghua University Department of Applied Mathematics Shanghai China College of Computer Science and Technology Jilin University Changchun China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Maternal and Child Medicine Southern Medical University Affiliated Shenzhen Maternity and Child Healthcare Hospital Shenzhen China Xinhua Hospital Affiliated Shanghai Jiao Tong University of Medicine Department of General Surgery Shanghai China Institute of Artificial Institute Donghua University Shanghai China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9798350358780

Spatial transcriptomics (ST) has enabled us elucidating tumor microenvironments, however, the technological limitations without single-cell resolution severely hinder its application. Here, we propose CellMirror, an interpretable contrastive learning model to decipher heterogeneous cell populations in ST data by single-cell RNA-sequencing (scRNA-seq) data. Specifically, CellMirror learns the disentangled shared (representing biological variations in both data) and salient features (specific to ST data) by two contrastive variational encoders, while constructing the relations between genes and features by a shared linear decoder. In various cancer samples, CellMirror outperforms other tools in learning common features for label transfer, and interpretation of features. Particularly, in breast cancer studies, CellMirror detects finer domains in ST data missed by other methods, and is robust to dissect cell populations in ST data using independent scRNA-seq data. These results demonstrate applications of CellMirror in interpreting the complex tumor structure in ST data by integrating scRNA-seq data. © 2023 IEEE.

关键词： Data integration

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