Hybrid of Harmony Search Algorithm and Ring Theory-Based Evolutionary Algorithm for Feature Selection
DC Field | Value | Language |
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dc.contributor.author | Ahmed S. | - |
dc.contributor.author | Ghosh K.K. | - |
dc.contributor.author | Singh P.K. | - |
dc.contributor.author | Geem Z.W. | - |
dc.contributor.author | Sarkar R. | - |
dc.date.available | 2020-07-21T08:35:22Z | - |
dc.date.created | 2020-06-22 | - |
dc.date.issued | 2020-06 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/68089 | - |
dc.description.abstract | Feature Selection (FS) is an important pre-processing step in the fields of machine learning and data mining, which has a major impact on the performance of the corresponding learning models. The main goal of FS is to remove the irrelevant and redundant features, resulting in optimized time and space requirements along with enhanced performance of the learning model under consideration. Many meta-heuristic optimization techniques have been applied to solve FS problems because of its superiority over the traditional optimization approaches. Here, we have introduced a new hybrid meta-heuristic FS model based on a well-known meta-heuristic Harmony Search (HS) algorithm and a recently proposed Ring Theory based Evolutionary Algorithm (RTEA), which we have named as Ring Theory based Harmony Search (RTHS). Effectiveness of RTHS has been evaluated by applying it on 18 standard UCI datasets and comparing it with 10 state-of-the-art meta-heuristic FS methods. Obtained results prove the superiority of RTHS over the state-of-the-art methods considered here for comparison. © 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 | Hybrid of Harmony Search Algorithm and Ring Theory-Based Evolutionary Algorithm for Feature Selection | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000546410800061 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2999093 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.8, pp.102629 - 102645 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85086440335 | - |
dc.citation.endPage | 102645 | - |
dc.citation.startPage | 102629 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 8 | - |
dc.contributor.affiliatedAuthor | Geem Z.W. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | feature selection | - |
dc.subject.keywordAuthor | harmony search | - |
dc.subject.keywordAuthor | hybrid optimization | - |
dc.subject.keywordAuthor | meta-heuristic | - |
dc.subject.keywordAuthor | ring theory based evolutionary algorithm | - |
dc.subject.keywordAuthor | Ring theory based harmony search | - |
dc.subject.keywordAuthor | UCI datasets | - |
dc.subject.keywordPlus | Algebra | - |
dc.subject.keywordPlus | Data mining | - |
dc.subject.keywordPlus | Feature extraction | - |
dc.subject.keywordPlus | Heuristic algorithms | - |
dc.subject.keywordPlus | Heuristic methods | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Optimization | - |
dc.subject.keywordPlus | Harmony search algorithms | - |
dc.subject.keywordPlus | Hybrid Meta-heuristic | - |
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 | Space requirements | - |
dc.subject.keywordPlus | State-of-the-art methods | - |
dc.subject.keywordPlus | Evolutionary algorithms | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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