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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.issn1432-7643
dc.identifier.doi10.1007/s00500-020-05183-1
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom12821
dc.relation.ispartofpageto12843
dc.relation.ispartofissue17
dc.relation.ispartofjournalSoft Computing
dc.relation.ispartofvolume24
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode4901
dc.subject.fieldofresearchcode5204
dc.subject.fieldofresearchcode46
dc.subject.keywordsScience & Technology
dc.subject.keywordsComputer Science, Interdisciplinary Applications
dc.titleEmbedded chaotic whale survival algorithm for filter-wrapper feature selection
dc.typeJournal article
dc.type.descriptionC1 - Articles
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. 12821-12843
dc.date.updated2020-10-27T02:55:17Z
dc.description.versionAccepted Manuscript (AM)
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.
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


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