A novel density-based adaptive k nearest neighbor method for dealing with overlapping problem in imbalanced datasets

Although a large number of solutions have been proposed to handle imbalanced classification problems over past decades, many researches pointed out that imbalanced problem does not degrade learning performance by its own but together with other factors. One of these factors is the overlapping problem which plays an even larger role in the classification performance deterioration but is always ignored in previous study. In this paper, we propose a density-based adaptive k nearest neighbor method, namely DBANN, which can handle imbalanced and overlapping problems simultaneously. To do so, a simple but effective distance adjustment strategy is developed to adaptively find the most reliable query neighbors. Concretely, we first partition training data into six parts by density-based method. Next, for each part, we modify distance metric by considering both local and global distribution. Finally, output is made by the query neighbors selected in the new distance metric. Noticeably, the query neighbors of DBANN are adaptively changed according to the degree of imbalance and overlap. To show the validity of our proposed method, experiments are carried out on 16 synthetic datasets and 41 real-world datasets. The results supported by the proper statistical tests show that our proposed method significantly outperforms the state-of-the-art methods.