Dynamic ensemble of rough set reducts for data classification

Abstract

Ensemble learning also named ensemble of multiple classifiers is one of the hot topics in machine learning. Ensemble learning can improve not only the accuracy but also the efficiency of the classification system. Constructing the component classifiers in ensemble learning is crucial, because it has direct influence on the performance of the classification system. In the construction of component classifiers, it should be guaranteed that the constructed component classifiers possess certain accuracy and diversity. Based on the confidence degree of classifier, this paper presents an approach consisting of three steps to dynamically integrate rough set reducts. Firstly, multiple reducts are computed. Secondly, multiple component classifiers with certain diversity are trained on the different reducts. Finally, these component classifiers are integrated by adopting dynamic integration strategy. The experimental results show that the proposed algorithm is efficient and feasible.