Iterative minority oversampling and its ensemble for ordinal imbalanced datasets

Abstract

Ordinal classification of imbalanced datasets is a challenging problem that occurs in many real-world applications. The main challenge is to simultaneously consider the classes ordering and imbalanced distribution. Although the classic synthetic instances oversampling techniques can improve the identification of minority classes, they easily incur the damage of the classes ordering when the synthetic instances fall in non-adjacent classes regions. In this paper, we propose a powerful method for handling the imbalanced problem embedded in the ordinal classification, namely Iterative Minority oversampling technique for imbalanced Ordinal Classification (IMOC). Concretely, we first develop an iterative identification procedure to select the minority instance that is hardest to learn. Then, a weighted oversampling probability distribution that respects the ordinal nature is used to generate synthetic minority instances to balance the skewed distribution. Furthermore, two novel ensemble versions are developed to boost the capability of our proposed IMOC. In order to verify the effectiveness and robustness of our proposed methods, an extensive experimental study is carried out on a large number of datasets from real-world applications. The experimental results supported by proper statistical tests indicate that our proposed methods outperform state-of-the-art algorithms in terms of the most frequently used performance measures.