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Multi-label learning with label-specific feature reduction
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DOI：10.1016/j.knosys.2016.04.012.png)
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摘要
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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.
Keyword:
Feature reduction
Fuzzy rough set
Label-specific feature
Multi-label learning
Sample selection
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7.6
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1.3W
被引数:
4.5W
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