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dc.contributor.authorGuha, Ritam
dc.contributor.authorGhosh, Manosij
dc.contributor.authorMutsuddi, Shyok
dc.contributor.authorSarkar, Ram
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2020-10-27T02:59:14Z
dc.date.available2020-10-27T02:59:14Z
dc.date.issued2020
dc.identifier.issn1432-7643en_US
dc.identifier.doi10.1007/s00500-020-05183-1en_US
dc.identifier.urihttp://hdl.handle.net/10072/398749
dc.description.abstractClassification accuracy provided by a machine learning model depends a lot on the feature set used in the learning process. Feature selection (FS) is an important and challenging preprocessing technique which helps to identify only the relevant features from a dataset, thereby reducing the feature dimension as well as improving the classification accuracy at the same time. The binary version of whale optimization algorithm (WOA) is a popular FS technique which is inspired from the foraging behavior of humpback whales. In this paper, an embedded version of WOA called embedded chaotic whale survival algorithm (ECWSA) has been proposed which uses its wrapper process to achieve high classification accuracy and a filter approach to further refine the selected subset with low computation cost. Chaos has been introduced in the ECWSA to guide selection of the type of movement followed by the whales while searching for prey. A fitness-dependent death mechanism has also been introduced in the system of whales which is inspired from the real-life scenario in which whales die if they are unable to catch their prey. The proposed method has been evaluated on 18 well-known UCI datasets and compared with its predecessors as well as some other popular FS methods. The source code of ECWSA can be found in https://github.com/Ritam-Guha/ECWSA.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.language.isoeng
dc.publisherSpringeren_US
dc.relation.ispartofpagefrom12821en_US
dc.relation.ispartofpageto12843en_US
dc.relation.ispartofissue17en_US
dc.relation.ispartofjournalSoft Computingen_US
dc.relation.ispartofvolume24en_US
dc.subject.fieldofresearchApplied Mathematicsen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processingen_US
dc.subject.fieldofresearchCognitive Sciencesen_US
dc.subject.fieldofresearchcode0102en_US
dc.subject.fieldofresearchcode0801en_US
dc.subject.fieldofresearchcode1702en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsComputer Science, Interdisciplinary Applicationsen_US
dc.titleEmbedded chaotic whale survival algorithm for filter-wrapper feature selectionen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationGuha, R; Ghosh, M; Mutsuddi, S; Sarkar, R; Mirjalili, S, Embedded chaotic whale survival algorithm for filter-wrapper feature selection, Soft Computing, 2020, 24 (17), pp. 12821-12843en_US
dc.date.updated2020-10-27T02:55:17Z
dc.description.versionAccepted Manuscript (AM)en_US
gro.rights.copyright© 2020 Springer. This is an electronic version of an article published in Soft Computing, 2020, 24 (17), pp. 12821-12843. American Journal of Cancer is available online at: http://link.springer.com/ with the open URL of your article.en_US
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


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