[1]HU Xiaosheng,WEN Juping,ZHONG Yong.Imbalanced data ensemble classification using dynamic balance sampling[J].CAAI Transactions on Intelligent Systems,2016,11(2):257-263.[doi:10.11992/tis.201507015]
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Imbalanced data ensemble classification using dynamic balance sampling

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 2 Page number: 257-263 Column: 学术论文—机器学习 Public date: 2016-04-25
Title:
Imbalanced data ensemble classification using dynamic balance sampling
Author(s):
HU Xiaosheng;  WEN Juping;  ZHONG Yong
College of Electronic and Information Engineering, Foshan University, Foshan 528000, China
Keywords:
data mining; imbalanced data; re-sampling; ensemble; random forest
CLC:
TP181
DOI:
10.11992/tis.201507015
Abstract:
Traditional classification algorithms assume balanced class distribution or equal misclassification costs, which result in poor predictive accuracy of minority classes when handling imbalanced data. A novel imbalanced data classification method that combines dynamic balance sampling with ensemble boosting classifiers is proposed. At the beginning of each iteration, each member of the dynamic balance ensemble is trained with under-sampled data from the original training set and is augmented by artificial instances obtained using SMOTE . The distribution proportion of each class sample is randomly chosen to reflect the diversity of the training data and to provide a better training platform for the ensemble sub-classifier. Once the sub-classifiers are trained, a strong classifier is obtained using a weighting vote. Experimental results show that the proposed method provides better classification performance than other approaches.
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