Abstract: Undersampling is a commonly-used method for data reconstruction. This method is used to solve the problem of imbalanced data classification. However, the traditional undersampling method often loses important sample information, and lacks stabilities of experimental results. To settle these two problems, this paper proposes an imbalanced fuzzy multiclass support vector machine algorithm based on class-overlap degree undersampling. This algorithm combines LOF local outlier factor and box-whisker plot to delete noise samples in the training datasets, then extracts support vectors based on class-overlap degree. Finally, the class-overlap degree of each sample is set as the membership value of this sample, and the fuzzy multiclass support vector machine is constructed. Experimental results show that our algorithm overcomes the disadvantages that the support vector machine with random undersampling often loses the important sample information and the unstabilities of experimental results. In addition, our algorithm improves the classification accuracy of support vector machine in imbalanced and noisy datasets.

Key words: support vector machine, fuzzy multiclass support vector machine, noise, imbalanced datasets, class-overlap degree