Computer Science ›› 2017, Vol. 44 ›› Issue (8): 225-229.doi: 10.11896/j.issn.1002-137X.2017.08.038

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Imbalanced Data Classification Method Based on Neighborhood Hybrid Sampling and Dynamic Ensemble

GAO Feng and HUANG Hai-yan   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The class imbalance problems severely affect the performance of the traditional classification algorithm,lea-ding to decrease the recognition rate of the minority.In order to solve this problem,a hybrid sampling technology based on neighborhood characteristic was proposed to enhance the classification accuracy of minority class.This technology changes the sampling weight according to the class distribution in the samples neighborhood,and uses the hybrid samp-ling to obtain the balanced data subset.Then the base classifiers are generated,for each test sample,a dynamic ensemble method based on local confidence is proposed to select the optimal base classifier sets.The experiments on UCI datasets show that the method has high classification accuracy rate of both minority and majority class for imbalance datasets.

Key words: Data mining,Imbalanced data,K-nearest neighbor,Hybrid sampling,Ensemble learning

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