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Multi-class imbalance learning problems suffering from the different distribution of classes occur in many real-world applications. One-versus-One (OVO) decomposition strategy is a common and useful technique used to address multi-class classification problems, which consists in dividing the original multi-class problem into all binary class sub-problems. The effort to reduce the effect of non-competent classifiers has proven to be a useful way of improving the performance in the OVO scheme. However, these approaches might not be effective for imbalance scenarios, since they are based on standard biased learning procedures. On this account, we propose a novel approach named Distance-based Relative Competence Weighting with Adaptive Synthetic Example Generation (DRCW-ASEG), which properly addresses the synergy between imbalance learning and dynamic classifier weighting in OVO scheme. This new proposed algorithm aims to dynamically produce synthetic examples of minority classes in the stage of dynamic weighting process. We develop a thorough experimental study in order to verify the benefits of the proposed algorithm considering different base binary classifiers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Class imbalance learning is one of the most important topics in the field of machine learning and data mining, and the Synthetic Minority Oversampling Techniques (SMOTE) is the common method to handle this issue. The main shortcomings of the classic SMOTE and its variants is the interpolation of potential noise and unrepresentative examples. This paper is devoted to proposing a novel parameter-free SMOTE mechanism to produce sufficient representative synthetic examples while avoiding interpolating noisy examples. Specifically, two types of minority class examples are defined, namely boundary and safe minority examples. The synthetic examples generation procedure fully reflects the characteristics of the minority class examples with filling the region dominated by the minority class and expanding the margin of the minority class. To verify the effectiveness and robustness of the proposed method, a thorough experimental study on forty datasets selected from real-world applications is carried out. The experimental results indicate that our proposed method is competitive to the classic SMOTE and its state-of-the-art variants. [ABSTRACT FROM AUTHOR]