Efficient Classification of Multi-label and Imbalanced Data using Min-Max Modular Classifiers

Many real-world applications, such as text categorization and subcellular localization of protein sequences, involve multi-label classification with imbalanced data. In this paper, we address these problems by using the min-max modular network. The min-max modular network can decompose a multi-label problem into a series of small two-class subproblems, which can then be combined by two simple principles. We also present several decomposition strategies to improve the performance of min-max modular networks. Experimental results on subcellular localization show that our method has better generalization performance than traditional SVMs in solving the multi-label and imbalanced data problems. Moreover, it is also much faster than traditional SVMs.