Finding Fuzzy-rough Reducts with Fuzzy Entropy

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Dataset dimensionality is undoubtedly the single most significant obstacle which exasperates any attempt to apply effective computational intelligence techniques to problem domains. In order to address this problem a technique which reduces dimensionality is employed prior to the application of any classification learning.Such feature selection (FS) techniques attempt to select a subset of the original features of a dataset which are rich in the most useful information. The benefits can include improved data visualisation and transparency, a reduction in training and utilisation times and potentially, improved prediction performance. Methods based on fuzzy-rough set theory have demonstrated this with much success.Such methods have employed the dependency function which is based on the information contained in the lower approximation as an evaluation step in the FS process. This paper presents three novel feature selection techniques employing fuzzy entropy to locate fuzzy-rough reducts. This approach is compared with two other fuzzy-rough feature selection approaches which utilise other measures for the selection of subsets.
Original languageEnglish
Number of pages7
Publication statusPublished - 01 Jun 2008
EventFuzzy Systems - Hong Kong, Hong Kong, China
Duration: 01 Jun 200806 Jun 2008
Conference number: 17


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2008
CityHong Kong
Period01 Jun 200806 Jun 2008


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