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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]

The class imbalance learning problem is an important topic that has attracted considerable attention in machine learning and data mining. The most common method of addressing imbalanced datasets is the synthetic minority oversampling technique (SMOTE). However, the SMOTE and its variants suffer from the noise derived from the interpolation of synthetic examples. In this paper, an overproduce-and-choose strategy, which is divided into the overproduction and selection phases, is proposed to generate an appropriate set of synthetic examples for imbalance learning problems. In the overproduction phase, a new interpolation mechanism is developed to produce numerous synthetic examples, while in the selection phase, the synthetic examples that are beneficial to the classification task are selected by using instance selection based on evolutionary computation. Experiments are conducted on a large number of datasets selected from the real-world applications. The experimental results demonstrate that the proposed method is significantly better than SMOTE and its well-known variants in terms of several metrics, including G-mean (GM) and area under the curve. [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]