关键词: 支持向量机, 不均衡数据, 过取样, 欠取样, K-近邻

Abstract: In order to resolve the over fitting phenomenon of classifiers and enhance classification performance, this paper proposes an under-sampling method for imbalanced data classification based on K-nearest neighbor in kernel space. Firstly, this algorithm computes the k nearest neighbors of samples and contrary class samples in kernel space, and computes the average distance between two class samples. Then, this algorithm deletes the samples away from the classification boundary according to the control parameters, and uses the SMOTE over-sampling algorithm for small class samples to generate a new balanced sample set. Finally, this algorithm gets the final decision function with the new dataset. The algorithm may resolve the problem of imbalanced dataset and improve the classification performance of SVM. The experimental results with artificial dataset and four groups of UCI datasets show that the algorithm is effective for imbalanced dataset.

Key words: support vector machine, imbalanced dataset, over-sampling, under-sampling, K-nearest neighbor