Fuzzy Entropy-Assisted Fuzzy-Rough Feature Selection


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dc.contributor.author Mac Parthaláin, Neil
dc.contributor.author Jensen, Richard
dc.contributor.author Shen, Qiang
dc.date.accessioned 2007-12-05T17:16:21Z
dc.date.available 2007-12-05T17:16:21Z
dc.date.issued 2007-12-05
dc.identifier.citation Mac Parthaláin , N , Jensen , R & Shen , Q 2007 , ' Fuzzy Entropy-Assisted Fuzzy-Rough Feature Selection ' . en
dc.identifier.other PURE: 82441
dc.identifier.other dspace: 2160/393
dc.identifier.uri http://hdl.handle.net/2160/393
dc.description N. Mac Parthalain, R. Jensen and Q. Shen. Fuzzy entropy-assisted fuzzy-rough feature selection. Proceedings of the 15th International Conference on Fuzzy Systems (FUZZ-IEEE'06). 2006. en
dc.description.abstract Feature Selection (FS) is a dimensionality reduction technique that aims to select a subset of the original features of a dataset which offer the most useful information. The benefits of feature selection include improved data visualisation, transparency, reduction in training and utilisation times and improved prediction performance. Methods based on fuzzy-rough set theory (FRFS) have employed the dependency function to guide the process with much success. This paper presents a novel fuzzy-rough FS technique which is guided by fuzzy entropy. The use of this measure in fuzzy-rough feature selection can result in smaller subset sizes than those obtained through FRFS alone, with little loss or even an increase in overall classification accuracy. en
dc.language.iso eng
dc.title Fuzzy Entropy-Assisted Fuzzy-Rough Feature Selection en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.description.status Non peer reviewed en

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