Incremental Perspective for Feature Selection Based on Fuzzy Rough Sets

Yanyan Yang, Degang Chen, H. Wang, Xizhao Wang

Research output: Contribution to journalArticlepeer-review

100 Citations (Scopus)
208 Downloads (Pure)


Feature selection based on fuzzy rough sets is an effective approach to select a compact feature subset that optimally predicts a given decision label. Despite being studied extensively, most existing methods of fuzzy rough set based feature selection are restricted to computing the whole dataset in batch, which is often costly or even intractable for large datasets. To improve the time efficiency, we investigate the incremental perspective for fuzzy rough set based feature selection assuming data can be presented in sample subsets one after another. The key challenge for the incremental perspective is how to add and delete features with the subsequent arrival of sample subsets. We tackle this challenge with strategies of adding and deleting features based on the relative discernibility relations that are updated as subsets arrive sequentially. Two incremental algorithms for fuzzy rough set based feature selection are designed based on the strategies. One updates the selected features as each sample subset arrives, and outputs the final feature subset where no sample subset is left. The other updates the relative discernibility relations but only performs feature selection where there is no further subset arriving. Experimental comparisons suggest our incremental algorithms expedite fuzzy rough set based feature selection without compromising performance.
Original languageEnglish
Pages (from-to)1257-1273
Number of pages17
JournalIEEE Transactions on Fuzzy Systems
Issue number3
Early online date27 Jun 2017
Publication statusPublished (in print/issue) - 31 May 2018


  • Attribute reduction
  • feature selection
  • fuzzy rough sets
  • incremental learning
  • relative discernibility relation


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