New mixed integer fractional programming problem and some multi-objective models for sparse optimization

We propose a novel Mixed-Integer Nonlinear Programming (MINLP) model for sparse optimization based on the polyhedral k-norm. We put special emphasis on the application of sparse optimization in Feature Selection for Support Vector Machine (SVM) classification. We address the continuous relaxation of...

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Vydáno v:	Soft computing (Berlin, Germany) Ročník 27; číslo 21; s. 15893 - 15904
Hlavní autoři:	Pirouz, Behzad, Gaudioso, Manlio
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Classification Computational Intelligence Control Engineering Feature selection Machine learning Mathematical Logic and Foundations Mathematical programming Mechatronics Mixed integer Multiple objective analysis Nonlinear programming Norms Objectives Optimization Parameter estimation Robotics Sparsity Support vector machines Sparse optimization Fractional programming Multi-objective machine learning Mixed integer problem norm
ISSN:	1432-7643, 1433-7479
On-line přístup:	Získat plný text
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