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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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 27; H. 21; S. 15893 - 15904
Hauptverfasser: Pirouz, Behzad, Gaudioso, Manlio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023
Springer Nature B.V
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08839-w