Universum parametric-margin ν-support vector machine for classification using the difference of convex functions algorithm

Sparse solution of least-squares twin multi-class support vector machine using ℓ0 and ℓp-norm for classification and feature selection

In the realm of multi-class classification, the twin K-class support vector classification (Twin-KSVC) generates ternary outputs {-1,0,+1} by evaluating all training data in a "1-versus-1-versus-rest" structure. Recently, inspired by the least-squares version of Twin-KSVC and Twin-KSVC, a new multi-class classifier called improvements on least-squares twin multi-class classification support vector machine (ILSTKSVC) has been proposed. In this method, the concept of structural risk minimization is achieved by incorporating a regularization term in addition to the minimization of empirical risk. Twin-KSVC and its improvements have an influence on classification accuracy. Another aspect influencing classification accuracy is feature selection, which is a critical stage in machine learning, especially when working with high-dimensional datasets. However, most prior studies have not addressed this crucial aspect. In this study, motivated by ILSTKSVC and the cardinality-constrained optimization problem, we propose ℓp-norm least-squares twin multi-class support vector machine (PLSTKSVC) with 0 Collapse

Key Words

• -norm
• Cardinality-constrained optimization problem
• Feature selection
• Least-squares
• Multi-class classification
• Twin k-class support vector classification

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MESH Headings

• Support Vector Machine
• Machine Learning
• Least-Squares Analysis

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Affiliation(s)

Hossein Moosaei Department of Informatics, Faculty of Science, Jan Evangelista Purkyně University, Ústí nad Labem, Czech Republic; Department of Econometrics, Prague University of Economics and Business, Czech Republic.
Milan Hladík Department of Applied Mathematics, School of Computer Science, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; Department of Econometrics, Prague University of Economics and Business, Czech Republic.

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