Imbalanced data classification procedure based on SMOTE

Abstract

Now that it is possible to handle the enormous amounts of data available on the Internet and generated by corporate systems, automatic classification technology has assumed greater importance. Various learning methods have been proposed for solving two-class classification problem. Furthermore, an ensemble learning method that uses a combination of a plurality of classifiers can deliver high accuracy. However, the imbalance of class labels, that is, the sample size between classes to be classified is greatly different, often occurs. It is reported that the ensemble learning method has poor accuracy in regard to such imbalanced data.
Therefore, in this research, we propose a novel analysis procedure based on ensemble learning for such imbalanced data. Our proposed procedure involves dividing data into several legions by clustering and uses the over-sampling technique to make data ease imbalanced state and learns classification rule based on the random forest method proposed by Breiman (2001). Through simulations, we show the effectiveness of the proposed procedure.