%0 Journal Article %A LU Miaofang %A YANG Youlong %T Oversampling Algorithm Based on Density Peak Clustering and Radial Basis Function %D 2022 %R 10.3778/j.issn.1002-8331.2103-0564 %J Computer Engineering and Applications %P 67-74 %V 58 %N 21 %X Most of the existing oversampling algorithms only consider the distribution of the minority instances but ignore the distribution of the majority instances in the sampling process. In addition to the problem of imbalance between classes, the data set also has the problem of imbalance within classes. To solve these problems, this paper proposes a new oversampling method based on density peak clustering and radial basis function. Firstly, the minority instances are adaptively clustered by the improved density peak clustering algorithm, and a number of minority sub-clusters are obtained. Secondly, the local density calculated by the clustering process is used to assign weights to each sub-cluster, which are used to determine the required number of each sub-cluster. Finally, the radial basis function is used to calculate the mutual minority class potential of each minority instances, and the minority class is oversampled based on the mutual minority class potential. The proposed algorithm is combined with different classifiers to conduct experiments, and different indicators are used to evaluate the performance. The experiment shows that the performance of the proposed algorithm is better. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2103-0564