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CCR-GSVM: A boundary data generation algorithm for support vector machine in imbalanced majority noise problem

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Abstract

In imbalanced data classification, training classification model with synthetic samples can effectively improve the performance in mining minority samples. However, the majority samples are more likely to produce noise samples than the minority. The noise samples of the majority will affect the data generation algorithm, and it’s difficult to generate the synthetic boundary data for the minority. A boundary synthetic data generation algorithm called CCR-GSVM is proposed based on Combined Cleaning and Resampling(CCR) and Granular Support Vector Machine(GSVM-RU) in this paper. CCR-GSVM combines the boundary information of SVM and GSVM-RU to filter the noise samples of the majority, so as to use more effective majority samples generate synthetic data by CCR. The synthetic samples located in the margin boundary are obtain if the classification performance is improved. The comparative experiments on 12 imbalanced datasets shows that the boundary data generated by CCR-GSVM can help support vector machine with effect on improving the F1-measure and G-mean under the situation of majority noise, which shows the CCR-GSVM has a better effectiveness to generate synthetic boundary samples.

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  1. College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China

    Kai Huang & Xiaoguo Wang

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  1. Kai Huang
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  2. Xiaoguo Wang
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Correspondence to Kai Huang.

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Huang, K., Wang, X. CCR-GSVM: A boundary data generation algorithm for support vector machine in imbalanced majority noise problem. Appl Intell 53, 1192–1204 (2023). https://doi.org/10.1007/s10489-022-03408-4

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