A novel embedded min-max approach for feature selection in nonlinear support vector machine classification

DOI10.1016/j.ejor.2020.12.009zbMath1487.68195arXiv2004.09863OpenAlexW3020644100MaRDI QIDQ2030496

Salvador Pineda, Juan M. Morales, Asunción Jiménez-Cordero

Publication date: 7 June 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.09863

zbMATH Keywords

min-max optimizationduality theorymachine learningfeature selectionnonlinear support vector machine classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Quadratic programming (90C20) Optimality conditions and duality in mathematical programming (90C46)

Related Items (6)

Concept-cognitive computing system for dynamic classification ⋮ Preference estimation under bounded rationality: identification of attribute non-attendance in stated-choice data using a support vector machines approach ⋮ Margin optimal classification trees ⋮ On clustering and interpreting with rules by means of mathematical optimization ⋮ Dendrograms, minimum spanning trees and feature selection ⋮ On Minimax Fractional Semi-Infinite Programming Problems with Applications

Uses Software

• GitHub

Cites Work

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• Integer programming models for feature selection: new extensions and a randomized solution algorithm
• High dimensional data classification and feature selection using support vector machines
• Functional-bandwidth kernel for support vector machine with functional data: an alternating optimization algorithm
• Feature selection for classification models via bilevel optimization
• Variable selection in classification for multivariate functional data
• Mixed integer linear programming for feature selection in support vector machine
• Optimal feature selection for support vector machines
• Mercer’s Theorem, Feature Maps, and Smoothing
• Classification model selection via bilevel programming
• Combined SVM-based feature selection and classification

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