MBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network based on modified binary salp swarm algorithm and feature selection

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dc.authoridZhan, Jianming/0000-0003-2510-9515
dc.contributor.authorWu, Xunjin
dc.contributor.authorZhan, Jianming
dc.contributor.authorLi, Tianrui
dc.contributor.authorDing, Weiping
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:45:54Z
dc.date.available2024-05-19T14:45:54Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn the era of big data, the demand for multivariate time series prediction has surged, drawing increased attention to feature selection and neural networks in machine learning. However, certain feature selection methods neglect the alignment between actual data sample differences and clustering results, while neural networks lack automatic parameter adjustment in response to changing target features. This paper presents the MBSSA-Bi-AESN model, a Bi-directional Adaptive Echo State Network that utilizes the modified salp swarm algorithm (MBSSA) and feature selection to address the limitations of manually set parameters. Initial feature subset selection involves assigning weights based on the consistency of clustering results with differences. Subsequently, the four critical parameters in the Bi-AESN model are optimized using MBSSA. The optimized Bi-AESN model and selected feature subset are then integrated for simultaneous model learning and optimal feature subset selection. Experimental analysis on eight datasets demonstrates the superior prediction accuracy of the MBSSA-Bi-AESN model compared to benchmark models, underscoring its feasibility, validity, and universality.en_US
dc.description.sponsorshipNNSFC [12271146, 12161036, 62176221, 61976120]en_US
dc.description.sponsorshipThe present work was in part supported by grants from the NNSFC (12271146; 12161036; 62176221; 61976120).en_US
dc.identifier.doi10.1007/s10489-024-05280-w
dc.identifier.endpage1733en_US
dc.identifier.issn0924-669X
dc.identifier.issn1573-7497
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85182438410en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1706en_US
dc.identifier.urihttps://doi.org10.1007/s10489-024-05280-w
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5387
dc.identifier.volume54en_US
dc.identifier.wosWOS:001142906200002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofApplied Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectMultivariate Time Seriesen_US
dc.subjectFeature Selectionen_US
dc.subjectBi-Directional Adaptive Echo State Networken_US
dc.subjectBinary Salp Swarm Algorithmen_US
dc.titleMBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network based on modified binary salp swarm algorithm and feature selectionen_US
dc.typeArticleen_US

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