Paper Info
Title
Fuzzy-rough feature selection based on λ-partition differentiation entropy
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.
Year
DOI
Venue
2017
10.1109/FSKD.2017.8392938
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Keywords
Field
DocType
Feature selection,Fuzzy-rough sets,λ-Partition differentiation entropy
Information system,Set theory,Feature selection,Computer science,Fuzzy logic,Algorithm,Rough set,Feature extraction,Artificial intelligence,Partition (number theory),Gauge (firearms),Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5386-2166-0
0
0.34
References 
Authors
14
4
Name
Order
Citations
PageRank
Qian Sun12810.81
Yanpeng Qu2297.46
Ansheng Deng323.72
Longzhi Yang418227.45