Improved binary sailfish optimizer based on adaptive β-Hill climbing for feature selection
DC Field | Value | Language |
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dc.contributor.author | Ghosh K.K. | - |
dc.contributor.author | Ahmed S. | - |
dc.contributor.author | Singh P.K. | - |
dc.contributor.author | Geem Z.W. | - |
dc.contributor.author | Sarkar R. | - |
dc.date.available | 2020-07-30T07:35:25Z | - |
dc.date.created | 2020-05-28 | - |
dc.date.issued | 2020-04 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/71795 | - |
dc.description.abstract | Feature selection (FS), an important pre-processing step in the fields of machine learning and data mining, has immense impact on the outcome of the corresponding learning models. Basically, it aims to remove all possible irrelevant as well as redundant features from a feature vector, thereby enhancing the performance of the overall prediction or classification model. Over the years, meta-heuristic optimization techniques have been applied for FS, as these are able to overcome the limitations of traditional optimization approaches. In this work, we introduce a binary variant of the recently-proposed Sailfish Optimizer (SFO), named as Binary Sailfish (BSF) optimizer, to solve FS problems. Sigmoid transfer function is utilized here to map the continuous search space of SFO to a binary one. In order to improve the exploitation ability of the BSF optimizer, we amalgamate another recently proposed meta-heuristic algorithm, namely adaptive β-hill climbing (Aβ HC) with BSF optimizer. The proposed BSF and A β BSF algorithms are applied on 18 standard UCI datasets and compared with 10 state-of-the-art meta-heuristic FS methods. The results demonstrate the superiority of both BSF and Aβ BSF algorithms in solving FS problems. The source code of this work is available in https://github.com/Rangerix/MetaheuristicOptimization. © 2013 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | IEEE Access | - |
dc.title | Improved binary sailfish optimizer based on adaptive β-Hill climbing for feature selection | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000549502200151 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2991543 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.8, pp.83548 - 83560 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85084954307 | - |
dc.citation.endPage | 83560 | - |
dc.citation.startPage | 83548 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 8 | - |
dc.contributor.affiliatedAuthor | Geem Z.W. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | adaptive β-hill climbing | - |
dc.subject.keywordAuthor | Binary sailfish optimizer | - |
dc.subject.keywordAuthor | feature selection | - |
dc.subject.keywordAuthor | hybrid optimization | - |
dc.subject.keywordAuthor | UCI dataset | - |
dc.subject.keywordPlus | Data mining | - |
dc.subject.keywordPlus | Feature extraction | - |
dc.subject.keywordPlus | Heuristic algorithms | - |
dc.subject.keywordPlus | Heuristic methods | - |
dc.subject.keywordPlus | Classification models | - |
dc.subject.keywordPlus | Meta heuristic algorithm | - |
dc.subject.keywordPlus | Meta-heuristic optimization techniques | - |
dc.subject.keywordPlus | Optimization approach | - |
dc.subject.keywordPlus | Pre-processing step | - |
dc.subject.keywordPlus | Redundant features | - |
dc.subject.keywordPlus | Sigmoid transfer function | - |
dc.subject.keywordPlus | State of the art | - |
dc.subject.keywordPlus | Optimization | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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