Imbalanced data classification improvement with combination of random subspace method and decision tree
HU Xiao-shen · Journal of Foshan University · 2013
In this paper, a novel hybrid method of combination improved random subspace(RSM) method and C4.5 decision tree algorithm is proposed. The proposed method constructs decision tree with C4.5 algorithm as a basic classifier, at the beginning of each iteration, just like in RSM, some features of the training data are removed, after removing a subset of the features, SMOTE is then applied to the dataset which is subsequently used to train the base classifier. In this way, a higher degree of variance and diversity training datasets for base classifier are constructed. The fusion of decisions and the outputs are determined by the vast majority of votes. Experimental results show that the proposed method provides better classification performance than other approaches on both minority and majority classes, and is effective and feasible to deal with the imbalanced datasets.