Fuzzy-rough feature selection based on λ-partition differentiation entropy

Qian Sun, Yanpeng Qu, Ansheng Deng, Longzhi Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Fuzzy-rough set theory is proven as an effective tool for feature selection. Whilst promising, many state-of-the-art fuzzy-rough feature selection algorithms are time-consuming when dealing with the datasets which have a large quantity of features. In order to address this issue, a λ-partition differentiation entropy fuzzy-rough feature selection (LDE-FRFS) method is proposed in this paper. Such λ-partition differentiation entropy extends the concept of partition differentiation entropy from rough sets to fuzzy-rough sets on the view of a partition of the information system. In this case, it can efficiently gauge the significance of features. Experimental results demonstrate that, by such λ-partition differentiation entropy-based attribute significance, LDE-FRFS outperforms the competitors in terms of both the size of the reduced datasets and the execute time.
Original languageEnglish
Title of host publication2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
PublisherIEEE
Pages1222-1227
Number of pages6
ISBN (Electronic)9781538621653
ISBN (Print)9781538621660
DOIs
Publication statusE-pub ahead of print - 25 Jun 2018
Event2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) - Guilin
Duration: 29 Jul 201731 Jul 2017

Conference

Conference2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Period29/07/1731/07/17

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