期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 47, 期 12, 页码 4263-4274出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2606104
关键词
Big data; class imbalance; ensemble; learning method; noise filter; resampling; under-sampling
类别
资金
- Natural Science Foundation of China [71371142, 61005090, 91546115, 71540022]
- FDCT (Fundo para o Desenvolvimento das Ciencias e da Tecnologia) [119/2014/A3]
- Fundamental Research Funds for the Central Universities
Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, F-measure, and G-mean.
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