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Sparse Solution of Least-squares Twin Multi-class Support Vector Machine Using L0 and Lp-norm for Classification and Feature Selection by Hossein Moosaei; Milan Hladík is a Computer Science article available to read on EtoBox.

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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 l p -norm least-squares twin multi-class support vector machine (PLSTKSVC) with 0 < p < 1 to perform classification and feature selection at the same time. The techniqu

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Author

Hossein Moosaei; Milan Hladík

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)