Abstract: The under-sampling and over-sampling are the common methods for solving the classification problem in an imbalanced data.This paper focuses on the overfitting or lose valuable samples problems brought by using a single sampling method.A mixed sampling method,namely MSQEA,based on quantum evolutionary algorithm is proposed.In MSQEA,the majority class samples and minority class samples are firstly encoded separately to form individuals of population in the quantum evolutionary algorithm,and then an appropriate candidate sampling subset is obtained through optimization iterations.After that,the majority samples in candidate subset are removed by under-sampling to avoid the problem of subsequent oversampling method to generate overmuch redundant samples.Then,an oversampling method is used to generate the minority samples.Additionally,in order to effectively evaluate the fitness of quantum individuals,clustering technique is used to cluster the dataset and the effective validation sets for the evaluation of individuals are obtained.Experiments are conducted to evaluate the performance of algorithm MSQEA.The imbalanced data sets are downloaded from KEEL website,and SMO,J48 and NB are used as classifiers to verify the performance of a classifier after data preprocessing by different sampling methods.Experimental results show that the classification performance of MSQEA is better than some state-of-the art sampling methods.

Key words: Classification, Imbalanced data, Mixed-sampling, Quantum evolutionary algorithm