An Enhanced Extreme Learning Machine Based on Square-Root Lasso Method

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dc.contributor.authorGenc, Murat
dc.date.accessioned2025-03-17T12:27:35Z
dc.date.available2025-03-17T12:27:35Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractExtreme learning machine (ELM) is one of the most notable machine learning algorithms with many advantages, especially its training speed. However, ELM has some drawbacks such as instability, poor generalizability and overfitting in the case of multicollinearity in the linear model. This paper introduces square-root lasso ELM (SQRTL-ELM) as a novel regularized ELM algorithm to deal with these drawbacks of ELM. A modified version of the alternating minimization algorithm is used to obtain the estimates of the proposed method. Various techniques are presented to determine the tuning parameter of SQRTL-ELM. The method is compared with the basic ELM, RIDGE-ELM, LASSO-ELM and ENET-ELM on six benchmark data sets. Performance evaluation results show that the SQRTL-ELM exhibits satisfactory performance in terms of testing root mean squared error in benchmark data sets for the sake of slightly extra computation time. The superiority level of the method depends on the tuning parameter selection technique. As a result, the proposed method can be considered a powerful alternative to avoid performance loss in regression problems .
dc.identifier.doi10.1007/s11063-024-11443-0
dc.identifier.issn1370-4621
dc.identifier.issn1573-773X
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85187104997
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s11063-024-11443-0
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2330
dc.identifier.volume56
dc.identifier.wosWOS:001162756500007
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGenc, Murat
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofNeural Processing Letters
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectExtreme learning machine
dc.subjectRidge
dc.subjectLasso
dc.subjectSquare-root lasso
dc.subjectRegression
dc.titleAn Enhanced Extreme Learning Machine Based on Square-Root Lasso Method
dc.typeArticle

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