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
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Shrnutí: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 the problem, which comes out in the form of a fractional programming problem (FPP). In particular, we consider a possible way for tackling FPP by reformulating it via a DC (Difference of Convex) decomposition. We also overview the SVM models and the related Feature Selection in terms of multi-objective optimization. The results of some numerical experiments on benchmark classification datasets are reported.
Bibliografie:ObjectType-Article-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08839-w