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-con...Show MoreMetadata
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.
Published in: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Date of Conference: 29-31 July 2017
Date Added to IEEE Xplore: 25 June 2018
ISBN Information:
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- IEEE Keywords
- Index Terms
- Information System ,
- Feature Selection Methods ,
- Set Theory ,
- Feature Selection Algorithm ,
- Experimental Procedures ,
- Classification Accuracy ,
- Running Time ,
- Time Complexity ,
- Decision Support System ,
- Fuzzy Set ,
- Feature Subset ,
- Fuzzy System ,
- Information Entropy ,
- Classification Decision ,
- Acceptable Time ,
- Upper Estimate ,
- Conditional Entropy ,
- Raw Information ,
- Fuzzy Decision
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Information System ,
- Feature Selection Methods ,
- Set Theory ,
- Feature Selection Algorithm ,
- Experimental Procedures ,
- Classification Accuracy ,
- Running Time ,
- Time Complexity ,
- Decision Support System ,
- Fuzzy Set ,
- Feature Subset ,
- Fuzzy System ,
- Information Entropy ,
- Classification Decision ,
- Acceptable Time ,
- Upper Estimate ,
- Conditional Entropy ,
- Raw Information ,
- Fuzzy Decision
- Author Keywords