Integrating new data balancing technique with committee networks for imbalanced data: GRSOM approach

To deal with imbalanced data in a classification problem, this paper proposes a data balancing technique to be used in conjunction with a committee network. The proposed data balancing technique is based on the concept of the growing ring self-organizing map (GRSOM) which is an unsupervised learning algorithm. GRSOM balances the data through growing new data on a well-defined ring structure, which is iteratively developed based on the winning node nearby the samples. Accordingly, the new balanced data still preserve the topology of the original data. The performance of our proposed method is evaluated using four real data sets from the UCI Machine Learning Repository and the classification performance is measured using the fivefold cross validation method. Classifiers with most common data balancing techniques, namely the Minority Over-Sampling Technique (SMOTE) and the Random under-sampling Technique (RT), are used as the baseline methods in this study. The results reveal that a committee of classifiers constructed using GRSOM performs at least as well as the baseline methods. The results also suggest that classifiers constructed using neural networks with the backpropagation algorithm are more robust than those using the support vector machine.