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In order to improve the classification accuracy of the minority samples, this paper proposed a novel resampling algorithm based on the improved density peaks clustering method, named IDP-SMOTE. First, it improved density peaks clustering algorithm by utilizing Box-Cox transformation and σ-rule for finding the clustering centers and outliers automatically. Second, it combined the improved density peaks clustering algorithm with SMOTE method. With removing the noisy data, the synthetic samples could be generated in the sub-class regions on the basis of the values of local density and nearest distance of the minority samples. The presented algorithm avoids the boundary ambiguity caused by over-sampling, improves the imbalance problem with-in class and reduces the learning difficulty of the boundary data. Meanwhile, it realizes automatic clustering and resampling, and avoids the interference of subjective factors. Through the contrast experiment, the proposed algorithm is effective and adaptive. [ABSTRACT FROM AUTHOR]