Semi-supervised multi-class Adaboost by exploiting unlabeled data |
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| Authors: | Enmin Song Dongshan Huang Guangzhi Ma Chih-Cheng Hung |
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| Affiliation: | 1. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China;2. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China;3. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China;4. Geographic Information System group, Department of Economics and Computer Sciences, Telemark University College, Bø i Telemark, N-3800, Norway;5. School of Systems, Management and Leadership, Faculty of Engineering and IT, University of Technology Sydney, CB11.06.217, Building 11, 81 Broadway, Ultimo, NSW 2007, (PO Box 123), Australia;6. Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro Gwangjin-gu, Seoul 05006, Republic of Korea;7. Department of Civil Engineering, Kangwon National University, Republic of Korea;8. Department of Civil Engineering, Gujarat Technological University, Nr.Visat Three Roads, Visat - Gandhinagar Highway, Chandkheda, Ahmedabad 382424, Gujarat, India;9. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Viet Nam;10. College of Geology and Environment, Xi''an University of Science and Technology, Xi''an 710054, China;11. Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Malaysia;1. School of Accountancy, Tianjin University of Finance and Economics, Tianjin, PR China;2. College of Tourism and Service Management, Nankai University, Tianjin, PR China;3. Faculty of Software and Information Science, Iwate Prefectural University, Iwate Japan;4. School of Economics and Management, Zhejiang Normal University, Jinhua, Zhejiang Province, PR China;5. Management School, Harbin Institute of Technology, Harbin, Heilongjiang Province, PR China |
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| Abstract: | Semi-supervised learning has attracted much attention in pattern recognition and machine learning. Most semi-supervised learning algorithms are proposed for binary classification, and then extended to multi-class cases by using approaches such as one-against-the-rest. In this work, we propose a semi-supervised learning method by using the multi-class boosting, which can directly classify the multi-class data and achieve high classification accuracy by exploiting the unlabeled data. There are two distinct features in our proposed semi-supervised learning approach: (1) handling multi-class cases directly without reducing them to multiple two-class problems, and (2) the classification accuracy of each base classifier requiring only at least 1/K or better than 1/K (K is the number of classes). Experimental results show that the proposed method is effective based on the testing of 21 UCI benchmark data sets. |
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