A hybrid algorithm applied to classify unbalanced data
C.Y. Lee, Mengmeng Yang, Le Chang, Z.-J. Lee · Networked Computing and Advanced Information Management · 2010
Unbalanced data, minority classes with few samples, present in many applications. It is difficult to solve the problems of unbalanced data by traditional methods. In this paper, a hybrid algorithm based on random over-sampling, decision tree (DT), particle swarm optimization (PSO) and feature selection is proposed to classify unbalanced data. The proposed algorithm has the ability to select beneficial feature subsets, automatically adjust values of parameter and obtain the best classification accuracy. The zoo dataset is used to test the performance for the proposed algorithm. From simulation results, the classification accuracy of the proposed algorithm outperforms other existing methods.