基于密度峰值聚类的不平衡数据过抽样方法OA北大核心CHSSCDCSSCICSTPCD

Most oversampling methods for imbalanced data rely on too many parameters,which are easy to generate noise and difficult to handle manifold data sets.Therefore,this paper proposes an oversampling method for imbalanced data based on density peak clustering(OVMEDPC).First,OVMEDPC uses density peak clustering(DPC)to reveal the spatial structure of imbalanced data.Second,OVMEDPC designs a density peak clustering-based noise filter to remove noise.Finally,OVMEDPC designs an in-terpolation method to create synthetic minority class samples.Experiments show that in terms of random forest classifiers,OVMEDPC outperforms 5 advanced over-sampling methods on F-measure and G-mean.