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Binary relevance for multi-label learning： an overview

Frontiers of Computer Science 2018年第2期12卷 191-202页

作者： Min-Ling ZHANG Yu-Kun LI Xu-Ying LIU Xin GENG School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China Collaborative Innovation Center for Wireless Communications Technology Nanjing 211100 China

Multi-label learning deals with problems where each example is represented by a single instance while being associated with multiple class labels simultaneously. Binary relevance is arguably the most intuitive solution for learning from multi-label examples. It works by decomposing the multi-label learning task into a number of independent binary learning tasks （one per class label）. In view of its potential weakness in ignoring correlations between labels, many correlation-enabling extensions to binary relevance have been proposed in the past decade. In this paper, we aim to review the state of the art of binary relevance from three perspectives. First, basic settings for multi-label learning and binary relevance solutions are briefly summarized. Second, representative strategies to provide binary relevance with label correlation exploitation abilities are discussed. Third, some of our recent studies on binary relevance aimed at issues other than label correlation exploitation are introduced. As a conclusion, we provide suggestions on future research directions.

关键词： machine learning multi-label learning binary relevance label correlation class-imbalance relative labeling-importance

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Software Defect Detection with ROCUS

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Journal of Computer Science & Technology 2011年第2期26卷 328-342页

作者：姜远黎铭周志华 National Key Laboratory for Novel Software Technology Nanjing University

Software defect detection aims to automatically identify defective software modules for efficient software test in order to improve the quality of a software system. Although many machine learning methods have been successfully applied to the task, most of them fail to consider two practical yet important issues in software defect detection. First, it is rather difficult to collect a large amount of labeled training data for learning a well-performing model; second, in a software system there are usually much fewer defective modules than defect-free modules, so learning would have to be conducted over an imbalanced data set. In this paper~ we address these two practical issues simultaneously by proposing a novel semi-supervised learning approach named Rocus. This method exploits the abundant unlabeled examples to improve the detection accuracy, as well as employs under-sampling to tackle the class-imbalance problem in the learning process. Experimental results of real-world software defect detection tasks show that Rocus is effective for software defect detection. Its performance is better than a semi-supervised learning method that ignores the class-imbalance nature of the task and a class-imbalance learning method that does not make effective use of unlabeled data.

关键词： machine learning data mining semi-supervised learning class-imbalance software defect detection

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Predicting Βeta-Turns and Βeta-Turn Types Using a Novel Over-Sampling Approach

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Journal of Biomedical Science and Engineering 2014年第11期7卷 927-940页

作者： Lan Anh T. Nguyen Xuan Tho Dang Tu Kien T. Le Thammakorn Saethang Vu Anh Tran Duc Luu Ngo Sergey Gavrilov Ngoc Giang Nguyen Mamoru Kubo Yoichi Yamada Kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Department of Computer Science Hue University of Education Hue Vietnam Institute of Science and Engineering Kanazawa University Kanazawa Japan

β-turn is one of the most important reverse turns because of its role in protein folding. Many computational methods have been studied for predicting β-turns and β-turn types. However, due to the imbalanced dataset, the performance is still inadequate. In this study, we proposed a novel over-sampling technique FOST to deal with the class-imbalance problem. Experimental results on three standard benchmark datasets showed that our method is comparable with state-of-the-art methods. In addition, we applied our algorithm to five benchmark datasets from UCI Machine Learning Repository and achieved significant improvement in G-mean and Sensitivity. It means that our method is also effective for various imbalanced data other than β-turns and β-turn types.

关键词： Beta-Turns Beta-Turn Types class-imbalance Over-Sampling

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