Computer Science ›› 2015, Vol. 42 ›› Issue (Z11): 63-66.

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Unbalanced Data Classification Algorithm Based on Clustering Ensemble Under-sampling

ZHANG Xiao-shan and LUO Qiang   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Imbalanced data exists widely in the real world,under such circumstances,most traditional classification algorithms assume the balanced data distribution,which results in the classification outcome offset to the majority class,so the effort is not ideal.The enhanced AdaBoost based on the clustering ensemble under-sampling technique was proposed in this paper.The algorithm firstly clusters the sample data by clustering ensemble,according to the sample weight.And the majority class from each cluster in certain proportion are randomly selected and then merge with all minority class to generate a balanced training set.By use of the AdaBoost algorithm framework,the algorithm gives different weight adjustment to the majority class and the minority class respectively,and selectes several base classifiers with better effect to get the final ensemble.The experiment result show that:this algorithm has a certain advantage dealing with unbalanced data classification.

Key words: Machine learning,Imbalanced data,Clustering ensemble,Under-sampling,Ensemble learning

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