Clustered intrinsic label correlations for multi-label classification |
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| Affiliation: | 1. Computer Science School, College of Management Academic Studies, Rishon LeZion, Israel;2. Software and Information Systems Engineering Department, Ben-Gurion University of the Negev, Be’er-Sheva, Israel;1. Warsaw University of Life Sciences, 02-787 Warsaw, Nowoursynowska 166, Poland;2. Warsaw University of Technology, Faculty of Electrical Engineering, Koszykowa 75, Warsaw, Poland;3. Military University of Technology, Faculty of Electronics, Kaliskiego 2, 00-908 Warsaw, Poland;4. Memorial Cancer Centre and Institute of Oncology, Roentgena 5, 02-781 Warsaw, Poland;5. Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), RIADI Laboratory, ISI, 2 Street Abou Rayhane Bayrouni, 2080 Ariana, Tunisia;1. Graduate School of Ecnonomics, University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan;2. Faculty of Economics, University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan;1. Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran;2. Department of Computer Engineering, University of Guilan, Rasht, Iran;3. Department of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran;1. Department of electronics and information engineering, Korea University Sejong Campus, Sejong 30019, Korea\n;2. Department of control and robotics engineering, Kunsan National University, Kunsan 54150, Korea;1. Buckingham Business School, University of Buckingham, Buckingham MK18 1EG, United Kingdom;2. Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada |
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| Abstract: | Currently a consensus on multi-label classification is to exploit label correlations for performance improvement. Many approaches build one classifier for each label based on the one-versus-all strategy, and integrate classifiers by enforcing a regularization term on the global weights to exploit label correlations. However, this strategy might be suboptimal since it may be only part of the global weights that support the assumption. This paper proposes clustered intrinsic label correlations for multi-label classification (CILC), which extends traditional support vector machine to the multi-label setting. The predictive function of each classifier consists of two components: one component is the common information among all labels, and the other component is a label-specific one which highly depends on the corresponding label. The label-specific one representing the intrinsic label correlations is regularized by clustered structure assumption. The appealing features of the proposed method are that it separates the common information and the label-specific information of the labels and utilizes clustered structures among labels represented by the label-specific parts. The practical multi-label classification problems can be directly solved by the proposed CILC method, such as text categorization, image annotation and sentiment analysis. Experiments across five data sets validate the effectiveness of CILC, compared with six well-established multi-label classification algorithms. |
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