Enhancing classification modeling through feature selection and smoothness: A conic-fused lasso approach integrated with mean shift outlier modelling

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dc.contributor.authorYerlikaya, Fatma Özkurt
dc.contributor.authorTaylan, Pakize
dc.date.accessioned2024-04-24T17:21:17Z
dc.date.available2024-04-24T17:21:17Z
dc.date.issued2024
dc.departmentDicle Üniversitesi, Fen Fakültesi, Matematik Bölümüen_US
dc.description.abstractOutlier detection and variable selection are among main objectives of statistical analysis. In our study, we address the outlier problem for classification by using the Mean Shift Outlier Model (CLMSOM). Since the MSOM has more coefficients than the linear regression model, the complexity of the model MSOM is high. Therefore, we consider feature selection for MSOM by using fused Lasso (FLasso), which is beneficial and helpful in the cases where the number of explanatory variables or features is greater than the sample size. FLasso is penalizing both the coefficients and their successive differences by the L1-norm, and it allows sparsity for both of them, while Lasso only allows the coefficients by considering a nonsmooth optimization problem. In this study, we take into account Iterated Ridge approximation which enables us to use a smooth optimization for FLasso problem. Generated smooth optimization problem is solved by using one of continuous optimization techniques called Conic Quadratic Programming (CQP), which is enabling the utilization of interior point methods. The newly developed method is called Conic FLasso for classification by MSOM (CFLasso-CLMSOM) and is applied to real world data set to show its performance.en_US
dc.identifier.citationYerlikaya, F. Ö. ve Taylan, P. (2025). Enhancing classification modeling through feature selection and smoothness: A conic-fused lasso approach integrated with mean shift outlier modelling. Journal of Dynamics and Games, 12(1), 1-23.
dc.identifier.doi10.3934/jdg.2024002
dc.identifier.issn2164-6066
dc.identifier.issn2164-6074
dc.identifier.urihttps://doi.org/10.3934/jdg.2024002
dc.identifier.urihttps://hdl.handle.net/11468/19431
dc.identifier.wosWOS:001144952700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoenen_US
dc.publisherAmer Inst Mathematical Sciences-Aimsen_US
dc.relation.ispartofJournal of Dynamics and Games
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectOutlieren_US
dc.subjectFused lassoen_US
dc.subjectMean shiften_US
dc.subjectClassificationen_US
dc.subjectConvex optimizationen_US
dc.titleEnhancing classification modeling through feature selection and smoothness: A conic-fused lasso approach integrated with mean shift outlier modellingen_US
dc.titleEnhancing classification modeling through feature selection and smoothness: A conic-fused lasso approach integrated with mean shift outlier modelling
dc.typeArticleen_US

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