IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Regular Section
Novel Improvements on the Fuzzy-Rough QuickReduct Algorithm
Javad Rahimipour ANARAKIMahdi EFTEKHARIChang Wook AHN
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2015 Volume E98.D Issue 2 Pages 453-456


Feature Selection (FS) is widely used to resolve the problem of selecting a subset of information-rich features; Fuzzy-Rough QuickReduct (FRQR) is one of the most successful FS methods. This paper presents two variants of the FRQR algorithm in order to improve its performance: 1) Combining Fuzzy-Rough Dependency Degree with Correlation-based FS merit to deal with a dilemma situation in feature subset selection and 2) Hybridizing the newly proposed method with the threshold based FRQR. The effectiveness of the proposed approaches are proven over sixteen UCI datasets; smaller subsets of features and higher classification accuracies are achieved.

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© 2015 The Institute of Electronics, Information and Communication Engineers
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