[1]GAO Qi,LI Deyu,WANG Suge.Multi-label attribute reduction based on fuzzy inconsistency pairs[J].CAAI Transactions on Intelligent Systems,2020,15(2):374-385.[doi:10.11992/tis.201905046]
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Multi-label attribute reduction based on fuzzy inconsistency pairs
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 2
Page number:
374-385
Column:
人工智能院长论坛
Public date:
2020-03-05
- Title:
- Multi-label attribute reduction based on fuzzy inconsistency pairs
- Keywords:
- multi-label data; attribute reduction; fuzzy inconsistency pairs; label weight; Kullback-Leibler divergence; the relationship of labels; fuzzy rough sets; distinguished matrix
- CLC:
- TP391
- DOI:
- 10.11992/tis.201905046
- Abstract:
- In real life, there is a large amount of multi-label data, and in multi-label data processing, attribute reduction is one of the important methods to solve the high-dimensional disaster of multi-label data. Because there is a relationship between labels, in this paper we firstly use the KL divergence metric to determine the relationship between labels, then define the label weight, and then combine the label weight to define the fuzzy inconsistency pairs. Finally, considering the distingishing ability of attributes to the fuzzy inconsistency pairs, we propose a multi-label attribute reduction algorithm based on fuzzy inconsistency pairs. Extensive experiments carried out on eight publicly available data sets verify effectiveness of the proposed algorithm named MLAR-FL by comparing it with some state-of-the-art approaches.
- References:
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