Learning Imbalanced Multi-class Data with Optimal Dichotomy Weights
Class-imbalance is very common in real data mining tasks. Previous studies focused on binary-class imbalance problem, whereas multi-class imbalance problem is more challenging. Error correcting output codes (ECOC) technique can be applied to class-imbalance problem, however, the standard ECOC aims at maximizing accuracy, ignoring the fact that, when class-imbalance is really a problem, the minority classes are more important than the majority classes. To enable ECOC to tackle multi-class imbalance, it is desired to have an appropriate code matrix, an effective learning strategy and a decoding strategy emphasizing the minority classes. In this paper, based on the aforementioned consideration, we propose the imECOC method which works on dichotomies to handle both the between-class imbalance and within-class imbalance. As the dichotomy classifiers contribute differently to the final prediction, imECOC assigns weights to dichotomies and uses weighted distance for decoding, where the optimal dichotomy weights are obtained by minimizing a weighted loss in favor of the minority classes. Experimental results on fourteen data sets show that, imECOC performs significantly better than many state-of-the-art multi-class imbalance learning methods, no matter whether multi-class F1, G-mean or AUC are used as evaluation measures.