Consistency Based Partial Label Learning Algorithm
TANG Caizhi, ZHANG Minling
School of Computer Science and Engineering, Southeast University, Nanjing 210096 Key Laboratory of Computer Network and Information Integration, Ministry of Education,Southeast University, Nanjing 210096
Abstract An essential strategy to solve the partial label problem is disambiguation. In most existing strategies, instances are individually disambiguated without the consideration of the relationships among instances. In this paper, a consistency based partial label learning (COPAL) algorithm is proposed assumpting that labels associated with similar instances are likely to be similar. Based on the above assumption, the labeling information of the instance itself and its neighboring instances are simultaneously utilized for disambiguation. Experiments on both artificial datasets and realworld datasets show the good generalization ability of COPAL.
Fund:Supported by National Natural Science Foundation of China (No.61573104,61222309), Program for New Century Excellent Talents in University of Ministry of Education of China (No.NCET-13-0130)
About author:: (TANG Caizhi(Corresponding author), born in 1990, master student. His research interests include machine learning and data mining.) (ZHANG Minling, born in 1979, Ph.D., professor. His research interests include machine learning and data mining.)
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