SVDD-based weighted oversampling technique for imbalanced and overlapped dataset learning

Abstract

Imbalanced dataset classification issue poses a major challenge on machine learning domain. Traditional supervised learning algorithms usually bias towards the majority class when handling imbalanced datasets, thus leading to poor classification results on the minority class. The learning task would become crucially difficult when there are overlapping and within-imbalance issues in imbalanced datasets, which are often the case and have been proven to severely deteriorate the classification performance relative to between-class imbalance. In this paper, we propose a novel SVDD boundary-based weighted oversampling approach (SVDDWSMOTE) for handling imbalanced and overlapped data. The proposed approach first applies support vector data description (SVDD) model with greater penalty constant for the minority class than the majority class to generate the class boundary, and then identifies those misclassified majority or few minority instances by the generated class boundary as potential overlapped or noisy ones and eliminates them. To address the within-balance issues, we propose a weight assignment strategy based on densities and the distances to the SVDD class boundary, which facilitates simultaneously combating between-class and within-class imbalance issues caused by complicated distribution. In addition, such a strategy also favors generating more synthetic minority instances for borderline and sparser instances which are usually informative to the later learning tasks. Finally, oversampling is performed by the weighed SMOTE scheme based on SVDD boundary to not only counteract the within imbalance but also avoid the generation of any noisy or overlapped synthetic instance. Extensive comparison results on various datasets show that the proposed approach achieves statistically significant improvements in terms of different classification performance metrics relative to state-of-the-art ones.