Hybridized Swarm Metaheuristics for Evolutionary Random Forest Generation

Abstract

In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining ant colony metaheuristics and evolutionary computing technique. The method benefits from the stochastic process and population approach, which allows the algorithm to evolve more efficiently than each method alone. As the method is similar to random forest generation, it can be also used for feature selection. The paper also discusses the parameter estimation for the method. Tests on real data (UCI and real biomedical data) have been performed and evaluated. The average accuracy of the method over MIT-BIH database with normalized data and equalized classes is sensitivity 93.22 % and specificity 87.13 %.