Journal
KNOWLEDGE-BASED SYSTEMS
Volume 104, Issue -, Pages 52-61Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2016.04.012
Keywords
Feature reduction; Fuzzy rough set; Label-specific feature; Multi-label learning; Sample selection
Categories
Funding
- National Natural Science Foundation of China [61572242, 61305058, 61373062, 61503160, 61502211]
- Key Program of National Natural Science Foundation of China [61233011]
- Natural Science Foundation of Jiangsu Province of China [BK20130471]
- Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province [2014002]
- Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province [OBDMA201501]
- Postdoctoral Science Foundation of China [2014M550293]
- Qing Lan Project of Jiangsu Province of China, Postgraduate Research Innovation Foundation of Jiangsu University of Science and Technology [YCX15S-10]
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In multi-label learning, since different labels may have some distinct characteristics of their own, multi label learning approach with label-specific features named LIFT has been proposed. However, the construction of label-specific features may encounter the increasing of feature dimensionalities and a large amount of redundant information exists in feature space. To alleviate this problem, a multi-label learning approach FRS-LIFT is proposed, which can implement label-specific feature reduction with fuzzy rough set. Furthermore, with the idea of sample selection, another multi-label learning approach FRS-SS-LIFT is also presented, which effectively reduces the computational complexity in label-specific feature reduction. Experimental results on 10 real-world multi-label data sets show that, our methods can not only reduce the dimensionality of label-specific features when compared with LIFT, but also achieve satisfactory performance among some popular multi-label learning approaches. (C) 2016 Elsevier B.V. All rights reserved.
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