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MK-MSVCR: An Efficient Multiple Kernel Approach to Multi-class Classification

Zijie Dong^{1, 2}, Fen Chen³ and Yu Zhang⁴

1. School of Mathematics and Statistics, Bigdata Modeling and Intelligent Computing research institute, Hubei University of Education
   Second Gaoxin Road, Wuhan, 430205, China
2. Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University
   Wuhan 430062, China
   zjdong07@163.com
3. School of Finance, Hubei University of Economics
   Wuhan, 430205, China
   fenfen chen@163.com
4. School of Mathematics and Statistics, Hubei University of Education
   Second Gaoxin Road, Wuhan, 430205, China
   romeozyu@163.com

Abstract

This paper introduces a novel multi-class support vector classification and regression (MSVCR) algorithm with multiple kernel learning (MK-MSVCR). We present a new MK-MSVCR algorithm based on two-stage learning (MK-MSVCR-TSL). The two-stage learning aims to make classification algorithms better when dealing with complex data by using the first stage of learning to generate ”representative” or ”important” samples. We first establish the fast learning rate of MK-MSVCR algorithm for multi-class classification with independent and identically distributed (i.i.d.) samples amd uniformly ergodic Markov chain (u.e.M.c.) smaples, and prove that MK-MSVCR algorithm is consistent. We show the numerical investigation on the learning performance of MK-MSVCR-TSL algorithm. The experimental studies indicate that the proposed MK-MSVCR-TSL algorithm has better learning performance in terms of prediction accuracy, sampling and training total time than other multi-class classification algorithms.

Key words

multi-class classification, multiple kernel learning, learning rate, support vector classification and regression

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS230124001D

Publication information

Volume 21, Issue 1 (January 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Dong, Z., Chen, F., Zhang, Y.: MK-MSVCR: An Efficient Multiple Kernel Approach to Multi-class Classification. Computer Science and Information Systems, Vol. 21, No. 1, 143–166. (2024), https://doi.org/10.2298/CSIS230124001D