Show simple item record Mac Parthaláin, Neil Shen, Qiang Jensen, Richard 2008-01-22T16:49:59Z 2008-01-22T16:49:59Z 2008-01-22
dc.identifier.citation Mac Parthaláin , N , Shen , Q & Jensen , R 2008 , ' Comparing Fuzzy-Rough and Fuzzy Entropy-assisted Fuzzy-Rough Feature Selection ' . en
dc.identifier.other PURE: 83621
dc.identifier.other dspace: 2160/451
dc.description N. Mac Parthalain, R. Jensen, and Q. Shen. Comparing Fuzzy-Rough and Fuzzy Entropy-assisted Fuzzy-Rough Feature Selection. Proceedings of the 6th Annual Workshop on Computational Intelligence (UKCI'06). 2006. en
dc.description.abstract Feature Selection (FS) methods based on fuzzy-rough set theory (FRFS) have employed the dependency function to guide the FS process with much success. More recently a method has been developed which uses fuzzy-entropy \cite{mjs06} to perform this task. Such use of fuzzy-entropy as an evaluation measure in fuzzy-rough feature selection can result in smaller subset sizes than those obtained through FRFS alone. However, it has also been observed that the fuzzy-entropy based FS technique (which does not select subsets based on dependency), also demonstrates remarkably similar dependency values to those of the fuzzy-rough method. This paper investigates the apparent similarity of the dependency values and attempts to discover if any correlation exists. Results are obtained using both fuzzy-rough FS (which is guided solely by the dependency value) and the fuzzy entropy-assisted fuzzy-rough FS technique. en
dc.language.iso eng
dc.title Comparing Fuzzy-Rough and 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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