Abstract: Undersampling is one of the mainstream data-level technologies to deal with imbalanced data.In recent years,researchers have proposed numerous undersampling methods,but most of them focus on how to select representative majority class samples to avoid the loss of informative data.However,how to maintain the structures of the original majority class in the process of undersampling is still an open challenge.To this end,an undersampling method for imbalanced data classification is proposed based on constructive neural network and data density.Firstly,it detects the majority local patterns with a simplified constructive process.Then,two sample selection strategies are designed to maintain the structure of the selected groups according to the original majority distribution information.Finally,to solve the problem that the randomness of local pattern learning may lead to non-optimal sampling results,the bagging technique is introduced to further improve the learning performance.Comparative experiments with 13 comparison methodson 59 datasets verify the effectiveness of the proposed method in terms of three metrics G-mean,AUC and F1-score.

Key words: Undersampling, Imbalanced data, Distribution density, Constructive neural network, Ensemble learning