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Hybrid classification algorithms based on boosting and support vector machines

Thiago Turchetti Maia (Vetta Technologies Ltda, Nova Lima, Brazil)
Antônio Pádua Braga (Post‐Graduate Program in Electrical Engineering, UFMG, Belo Horizonte, Brazil)
André F. de Carvalho (ICMC, São Paulo University at São Carlos, São Carlos, Brazil)

Kybernetes

ISSN: 0368-492X

Article publication date: 17 October 2008

514

Abstract

Purpose

To create new hybrid algorithms that combine boosting and support vector machines to outperform other known algorithms in selected contexts of binary classification problems.

Design/methodology/approach

Support vector machines (SVM) are known in the literature to be one of the most efficient learning models for tackling classification problems. Boosting algorithms rely on other classification algorithms to produce different weak hypotheses which are later combined into a single strong hypothesis. In this work the authors combine boosting with support vector machines, namely the AdaBoost.M1 and sequential minimal optimization (SMO) algorithms, to create new hybrid algorithms that outperform standard SVMs in selected contexts. This is achieved by integration with different degrees of coupling, where the four algorithms proposed range from simple black‐box integration to modifications and mergers between AdaBoost.M1 and SMO components.

Findings

The results show that the proposed algorithms exhibited better performance for most problems experimented. It is possible to identify trends of behavior bound to specific properties of the problems solved, where one may hence apply the proposed algorithms in situations where it is known to succeed.

Research limitations/implications

New strategies for combining boosting and SVMs may be further developed using the principles introduced in this paper, possibly resulting in other algorithms with yet superior performance.

Practical implications

The hybrid algorithms proposed in this paper may be used in classification problems with properties that they are known to handle well, thus possibly offering better results than other known algorithms in the literature.

Originality/value

This paper introduces the concept of merging boosting and SVM training algorithms to obtain hybrid solutions with better performance than standard SVMs.

Keywords

Citation

Turchetti Maia, T., Pádua Braga, A. and de Carvalho, A.F. (2008), "Hybrid classification algorithms based on boosting and support vector machines", Kybernetes, Vol. 37 No. 9/10, pp. 1469-1491. https://doi.org/10.1108/03684920810907814

Publisher

:

Emerald Group Publishing Limited

Copyright © 2008, Emerald Group Publishing Limited

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