Random Resampling in the One-Versus-All Strategy for Handling Multi-class Problems

One of the most common approaches for handling the multiclass classification problem is to divise the original data set into binary subclasses and to use a set of binary classifiers in order to solve the binarization problem. A new method for solving multi-class classification problems is proposed, by incorporating random resampling techniques in the one-versus-all strategy. Specifically, the division used by the proposed method is based on the one-versus-all binarization technique using random resampling for handling the class-imbalance problem arising due to the one-versus-all binarization. The method has been tested extensively on several multiclass classification problems using Support Vector Machines with four different kernels. Experimental results show that the proposed method exhibits a better performance compared to the simple one-versus-all.