Combining Random Subspace Approach with smote Oversampling for Imbalanced Data Classification

Abstract

Following work tries to utilize a hybrid approach of combining Random Subspace method and smote oversampling to solve a problem of imbalanced data classification. Paper contains a proposition of the ensemble diversified using Random Subspace approach, trained with a set oversampled in the context of each reduced subset of features. Algorithm was evaluated on the basis of the computer experiments carried out on the benchmark datasets and three different base classifiers.