Classification Based on Fireworks Algorithm

Abstract

Data classification has attracted many researchers’ attention. Many evolutionary algorithms (EAs) were employed to take advantage of their global search ability. In supervised classification research issues, EAs were only used to improve the performance of classifiers either by optimizing the parameters or structure of the classifiers, or by pre-processing the inputs of the classifiers. Although genetic programming or evolutionary based decision tree approaches are proposed for classification, the development of these approaches is limited by their special structure. In this paper, we propose a new mathematical optimization model for the supervised classification problem and use fireworks algorithm (FWA) to do classification directly without modification. In the new optimization model, a linear equations set is constructed based on training set, and we propose an objective function which can be optimized by FWA. Four different data sets have been employed in the experiments, and 70% samples are used as train sets while the rest are used as test sets. The results show that the label of test sets can be identified accurately by our new methods. This paper also shows that the optimization model can be used for classification and employing EA to solve this optimization model is feasible.