Combating Mutuality with Difficulty Factors in Multi-class Imbalanced Data: A Similarity-based Hybrid Sampling

Multi-class imbalanced problem widely exists in real-life applications and has been a challenging issue. Existing sampling methods including decomposition approaches and dedicated approaches have limitations in handling the complex mutual relationships along with data difficulty factors. Actually, the relative minorities are critical in mutual relationship, and the data difficulty factors are harmful for these minority classes. In this paper, we propose SHSampler, a similarity-based hybrid sampling to combat the mutuality by addressing data difficulty factors in multi-class imbalanced data. Specifically, SHSampler firstly utilizes a sample similarity and dissimilarity estimation to identify data difficulty factors. Then, SHSampler conducts a relative majority weakening undersampling and a relative minority strengthening oversampling to reduce the negative impact of data difficulty factors and highlight the importance of the minorities. Extensive experiments over 20 typical datasets demonstrate the superiority of SHSampler in terms of MAUC and mGM when compared with 6 state-of-the-art methods.