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Binary arithmetic optimization algorithm for feature selection in pattern recognition

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dc.contributor.authorGeng, F.D.
dc.contributor.authorWang, R.B.
dc.contributor.authorWei, Q.
dc.contributor.authorXu, L.
dc.contributor.conferenceInternational Conference on Machine Learning and Cybernetics, ICMLC 2023 (9 Jul 2023 - 11 Jul 2023 : Adelaide, Australia)
dc.date.issued2023
dc.description.abstractFeature selection provides a technical way for pattern recognition to identify the important features from a dataset. However, Arithmetic Optimization Algorithm (AOA), which shows competence in solving continuous optimization problems, still cannot be employed in feature selection directly. In this paper, we propose a binary version of Arithmetic Optimization Algorithm (BAOA) that introduces a novel V-shaped time-varying transfer function to implement the transition from continuous search space to binary space. The proposed BAOA is proved to have superior performance in terms of convergence speed and ability to escape local optima. A set of evaluation indicators are employed to evaluate and compared the different algorithm over 15 datasets from the UCI repository. The simulation results prove the capability of the proposed binary version of the Arithmetic Optimization Algorithm to search for the optimal subset of features
dc.identifier.citationProceedings / International Conference on Machine Learning and Cybernetics. International Conference on Machine Learning and Cybernetics, 2023, pp.217-222
dc.identifier.doi10.1109/ICMLC58545.2023.10327990
dc.identifier.isbn9798350303780
dc.identifier.issn2160-133X
dc.identifier.issn2160-1348
dc.identifier.urihttps://hdl.handle.net/11541.2/37281
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeUS
dc.rightsCopyright 2023 IEEE
dc.source.urihttps://doi.org/10.1109/ICMLC58545.2023.10327990
dc.subjectarithmetic optimization algorithm
dc.subjectbinary
dc.subjectfeature selection
dc.subjectpattern recognition
dc.titleBinary arithmetic optimization algorithm for feature selection in pattern recognition
dc.typeConference paper
pubs.publication-statusPublished
ror.mmsid9916813825001831

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