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.

No abstract

The Min-Max Modular (M 3) Network is the convention solution method to large-scale and complex classification problems. We propose a new module combination strategy using a decision tree for the min-max modular network. Compared with min-max module combination method and its component classifier selection algorithms, the decision tree method has lower time complexity in prediction and better generalizing performance. Analysis of parallel subproblem learning and prediction of these different module combination methods of M 3 network show that the decision tree method is easy in parallel.