School of Computer and Information Technology, Shanxi University, Taiyuan 030006 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract The data in multi-label classification tasks are usually high dimensional. Utilizing high-dimension data directly for modeling often results in a lower training efficiency or a complex model with the classifier performance reduced. For multi-label data, the concept of attribute-label matrix is proposed, a label relation based fuzzy rough set model is established, and a reduction algorithm of the model is then designed for feature selection of multi-label classification tasks. Finally, the effectiveness of the proposed method is verified on eight public datasets.
Fund:Supported by National Natural Science Foundation of China(No.61672331,61632011,61573231,61432011,U1435212)
About author:: GUO Rongchao, born in 1993, master student. Her research interests include rough sets and multi-label learning.) (LI Deyu(Corresponding author), born in 1965, Ph.D., professor. His research inte-rests include granular computing and machine learning.) (WANG Suge, born in 1964, Ph.D., professor. Her research interests include natural language processing and text mining.)
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2017, Vol. 30 
