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Novel Improvements on the Fuzzy-Rough QuickReduct Algorithm
Javad Rahimipour ANARAKI Mahdi EFTEKHARI Chang Wook AHN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2015/02/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Pattern Recognition
fuzzy-rough set, dependency degree, feature selection, fuzzy-rough quickreduct,
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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.