ARSGOS Algorithm for Imbalanced Data Sets
There widely exists the class imbalanced data in the real world and the classification of the class imbalance become a hot research topic in the field of data mining. We propose a method named ARSGOS,which is an over-sampling algorithm based on the distribution of Gaussian mixture model( GMM) combined with adaptive random subspace ensemble. The algorithm is based on the idea of random subspace,and estimates the distribution of the minority class with GMM in different subspaces. Then the artificial data of minority class is generated according to the probability function estimated,so that the new instances are more accurate and real,avoiding generating noisy instances. The proposed algorithm increases the diversity in the procedure of the sampling and learning with the overlapping rate of the subsets,and improves the identification ability of the minority class in the imbalance data. We compare ARSGOS with the existing re-sampling methods based on different base classifiers on the UCI data sets. The experimental results show that ARSGOS improves the accuracy of the minority class and the overall classi? cation performance effectively.