SVDD boundary and DPC clustering technique-based oversampling approach for handling imbalanced and overlapped data

Imbalanced datasets classification remains an important domain in machine learning. Conventional supervised learning algorithms tend to be biased towards the majority class when addressing imbalanced datasets, thus providing poor classification results on the minority class. The learning task would become more challenging when there are overlapping and within-imbalance issues in imbalanced datasets, which are often the case and have been proven to play more negative impact on the classification performance than between-class imbalance. In this paper, we propose a novel SVDD (Support Vector Data Description) boundary and DPC (Density Peaks Clustering) clustering technique-based oversampling approach (SVDDDPCO) for handling imbalanced and overlapped data. The proposed approach first utilizes 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 boundary as potential overlapped or noisy ones and eliminates them. To address the within-balance issues, density peaks clustering (DPC) is used to cluster the minority instances due to its advantage of accurately identifying sub-clusters with different sizes and densities, which facilitates simultaneously combating between-class and within-class imbalance issues caused by various reasons. The minority instances are assigned with different weights inversely proportional to their local densities and their distances to the boundary and then the size for each identified sub-cluster to be oversampled is adaptively determined according to its own size and the weights of its elements. Such strategy aims to generate more synthetic minority instances for borderline and sparser small sub-clusters which are usually informative to the later learning tasks. Finally, oversampling is performed within each sub-cluster with different sizes 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 showed the proposed method achieves statistically significant improvements in classification performance relative to state-of-the-art ones.