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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1502

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dc.contributor.authorBhatt, Rajen B-
dc.contributor.authorGopal, M-
dc.date.accessioned2006-03-23T07:01:43Z-
dc.date.available2006-03-23T07:01:43Z-
dc.date.issued2005-
dc.identifier.citationPattern Recognition Letters, 26(7), 965-975en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/1502-
dc.description.abstractIn this paper, we have shown that the fuzzy-rough set attribute reduction algorithm [Jenson, R., Shen, Q., 2002. Fuzzy-rough sets for descriptive dimensionality reduction. In: Proceedings of IEEE International Conference on Fuzzy Systems, FUZZ-IEEE’02, May 12–17, pp. 29–34] is not convergent on many real datasets due to its poorly designed termination criteria; and the computational complexity of the algorithm increases exponentially with increase in the number of input variables and in multiplication with the size of data patterns. Based on natural properties of fuzzy t-norm and t-conorm, we have put forward the concept of fuzzy-rough sets on compact computational domain, which is then utilized to improve the computational efficiency of FRSAR algorithm. Speed up factor as high as 622 have been achieved with this concept with improved accuracy. We also restructure the algorithm with efficient termination criteria to achieve the convergence on all the datasets and to improve the reliability of selected set of features.en
dc.format.extent532290 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.subjectComputational complexityen
dc.subjectFeature selectionen
dc.subjectFuzzy setsen
dc.subjectRough setsen
dc.titleOn fuzzy-rough sets approach to feature selectionen
dc.typeArticleen
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