The selection method for hyper‐parameters of support vector classification by adaptive chaotic cultural algorithm

Abstract

Purpose

The purpose of this paper is to find a novel optimization selection method for hyper‐parameter of support vector classification (SVC), responsible for the classification of datasets from the UCI machine learning database repository.

Design/methodology/approach

A novel two‐stage optimization selection method for hyper‐parameters is proposed. It makes use of explicit information derived from issues and implicit knowledge extracted from the evolution process so as to improve the performance of classifier. In the first stage, the search extent of each hyper‐parameter is determined according to the requirements of issues. In the second stage, optimal hyper‐parameters are obtained by adaptive chaotic culture algorithm in the above search extent. Adaptive chaotic cultural algorithm uses implicit knowledge extracted from the evolution process to control mutation scale of chaotic mutation operator. This algorithm can ensure the diversity of population and exploitation in the latter evolution.

Findings

The rationality of the above optimization selection method is proved by the binary classification problem. Final confirmation of this approach is the classification results compared with other methods.

Originality/value

This optimization selection method can effectively avoid premature convergence and lead to better computation stability and precision. It is not related on the structure of functions. SVC model corresponding to optimal hyper‐parameters by this method has better generalization.

Citation

Guo, Y., Yang, M. and Xiao, D. (2010), "The selection method for hyper‐parameters of support vector classification by adaptive chaotic cultural algorithm", International Journal of Intelligent Computing and Cybernetics, Vol. 3 No. 3, pp. 449-462. https://doi.org/10.1108/17563781011066729