An efficient gaussian kernel based fuzzy-rough set approach for feature selection

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dc.contributor.author Ghosh, Soumen
dc.contributor.author Sai Prasad, P. S.V.S.
dc.contributor.author Raghavendra Rao, C.
dc.date.accessioned 2022-03-27T05:59:00Z
dc.date.available 2022-03-27T05:59:00Z
dc.date.issued 2016-01-01
dc.description.abstract Fuzzy-rough set based feature selection is highly useful for reducing data dimensionality of a hybrid decision system, but the reduct computation is computationally expensive. Gaussian kernel based fuzzy rough sets merges kernel method to fuzzy-rough sets for efficient feature selection. This works aims at improving the computational performance of existing reduct computation approach in Gaussian kernel based fuzzy rough sets by incorporation of vectorized (matrix, sub-matrix) operations. The proposed approach was extensively compared by experimentation with the existing approach and also with a fuzzy rough set based reduct approaches available in Rough set R package. Results establish the relevance of proposed modifications.
dc.identifier.citation Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). v.10053 LNAI
dc.identifier.issn 03029743
dc.identifier.uri 10.1007/978-3-319-49397-8_4
dc.identifier.uri http://link.springer.com/10.1007/978-3-319-49397-8_4
dc.identifier.uri https://dspace.uohyd.ac.in/handle/1/9007
dc.subject Feature selection
dc.subject Fuzzy-rough set
dc.subject Gaussian kernel
dc.subject Hybrid decision system
dc.subject Reduct computation
dc.subject Rough set
dc.title An efficient gaussian kernel based fuzzy-rough set approach for feature selection
dc.type Book Series. Conference Paper
dspace.entity.type
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