An Under-sampling Ensemble Classification Algorithm Based on Fuzzy C-Means Clustering for Imbalanced Data
Lianjie Xiao(),Mengrui Gao,Xinning Su
School of Information Management, Nanjing University, Nanjing 210023, China Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
[Objective] This paper tries to solve the problem of the low accuracy of minority classification in the binary classification task due to class imbalance. [Methods] An under-sampling ensemble classification algorithm based on fuzzy c-means(FCM) clustering for imbalanced data is proposed. That is, the majority class samples are under-sampled based on FCM clustering, all these cluster center samples and all the minority samples are made up to a balance data set. We use the integrated learning algorithm based on Bagging to classify the balanced data sets. [Results] The Matlab simulation results of experiments on four imbalanced datasets show that the ECFCM algorithm improves Acc, AUC and F1 by up to 5.75%, 13.84% and 7.54%. [Limitations] Some standard data sets are used to verify the effectiveness of ECFCM. When in a specific application, a targeted research on classification algorithm is needed. [Conclusions] The ECFCM algorithm performs good to a certain extent, which is conducive to improve the binary classification accuracy of the minority class on imbalanced datasets.
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