GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data
Iwo Naglik, Mateusz Lango
Abstract
Abstract Learning from multi-class imbalanced data has still received limited research attention. Most of the proposed methods focus on the global class imbalance ratio only. In contrast, experimental studies demonstrated that the imbalance ratio itself is not the main difficulty in the imbalanced learning. It is the combination of the imbalance ratio with other data difficulty factors, such as class overlapping or minority class decomposition into various subconcepts, that significantly affects the classification performance. This paper presents GMMSampling—a new resampling method that exploits information about data difficulty factors to clear class overlapping regions from majority class instances and to simultaneously oversample each subconcept of the minority class. The experimental evaluation demonstrated that the proposed method achieves better results in terms of G-mean, balanced accuracy, macro-AP, MCC and F-score than other related methods.