Adjusted bat algorithm for tuning of support vector machine parameters

Abstract

Support vector machines are powerful and often used technique of supervised learning applied to classification. Quality of the constructed classifier can be improved by appropriate selection of the learning parameters. These parameters are often tuned using grid search with relatively large step. This optimization process can be done computationally more efficiently and more precisely using stochastic search metaheuristics. In this paper we propose adjusted bat algorithm for support vector machines parameter optimization and show that compared to the grid search it leads to a better classifier. We tested our approach on standard set of benchmark data sets from UCI machine learning repository. Additionally, proposed algorithm was compared to other approaches from the literature where our algorithm obtained more accurate classifiers, with larger percent of correct classifications, especially in the more realistic cases where separate test sets were used instead of cross validation only.