Journal of Guangxi Normal University(Natural Science Edition) ›› 2019, Vol. 37 ›› Issue (2): 75-81.doi: 10.16088/j.issn.1001-6600.2019.02.009

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Improving Classification Rule with Lift Measure for KNN Classifier

WU Hao1*, QIN Lichun2, LUO Liurong2   

  1. 1.College of Computer Science and Information Technology, Guangxi Normal University, Guilin Guangxi 541004, China;
    2.Liuzhou Railway Vocational Technology College, Liuzhou Guangxi 545616, China
  • Received:2018-11-02 Online:2019-04-25 Published:2019-04-28

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Abstract: A KNN classifier is presented for classifying imbalanced data. A gain model is constructed for measuring the lift of probability of a class label. The competition of minority class is well enhanced in imbalanced-class dataset. And the accurate rate of classifying minor-class data is significantly improved. The experimental results show that in the setting of imbalanced-class datasets, the proposed approach has significantly improved the classification accuracy, compared with the existing KNN classifiers.

Key words: classification, KNN algorithm, imbalanced data, lift

CLC Number: 

  • TP181
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