Learning imbalanced data sets with a min-max modular support vector machine

To overcome the class imbalance problem in statistical machine learning research area, re-balancing the learning task is one of the most classical and intuitive approach. Besides re-sampling, many researchers consider task decomposition as an alternative method for re-balance. Min-max modular support vector machine combines both intelligent task decomposition methods and the min-max modular network model as classifier ensemble. It overcomes several shortcomings of re-sampling, and could also achieve fast learning and parallel learning. We compare its classification performance with resampling and cost sensitive learning on several imbalanced data sets from different application areas. The experimental results indicate that our method can handle class imbalance problem efficiently.