Abstract
A novel method, namely ensemble support vector machine with segmentation (SeEn–SVM), for the classification of imbalanced datasets is proposed in this paper. In particular, vector quantization algorithm is used to segment the majority class and hence generates some small datasets that are of less imbalance than original one, and two different weighted functions are proposed to integrate all the results of basic classifiers. The goal of the SeEn–SVM algorithm is to improve the prediction accuracy of the minority class, which is more interesting for people. The SeEn–SVM is applied to six UCI datasets, and the results confirmed its better performance than previously proposed methods for imbalance problem.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 10971223, No. 11071252) and Chinese Universities Scientific Fund (2011JS039, 2012YJ130). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Li, Q., Yang, B., Li, Y. et al. Constructing support vector machine ensemble with segmentation for imbalanced datasets. Neural Comput & Applic 22 (Suppl 1), 249–256 (2013). https://doi.org/10.1007/s00521-012-1041-z
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DOI: https://doi.org/10.1007/s00521-012-1041-z