Adaptive Undersampling Based on Density Peak Clustering
CUI Caixia1,2, CAO Fuyuan1,3 , LIANG Jiye1,3
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006 2. Computer Science and Technology Department, Taiyuan Normal University, Jinzhong 030619 3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract：Undersampling based on K-means clustering is only suitable for hypersphere shape data, the impact of overlapping regions on classification is not taken into account, and the density of samples in the clusters is neglected. Therefore, an adaptive undersampling method based on density peak clustering is proposed. Firstly, the samples of the majority class in the overlapping region are identified by the nearest neighbor search algorithm and deleted. Secondly, a number of clusters of different shapes, sizes and densities are automatically obtained by improved density peaks clustering. Then, undersampling is performed according to the sampling weights calculated by the density of the samples in the subclusters, and bagging ensemble classification is conducted on the obtained balanced dataset. Experiments indicate that the performance of the proposed method is better on most datasets.
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2020, Vol. 33 
