关键词: 非平衡数据集, 商空间理论, 支持向量机, 过采样, QMSVM算法

Abstract: The application of data classification is usually confronted with a problem named imbalanced dataset in the machine learning. To improve the performance of imbalanced dataset classification, the over-sampling classification algorithm based on quotient space theory (QMSVM) was proposed. The algorithm partitioned majority data on clustering structure, and combined the results and minority data for linear Support Vector Machine (SVM) learning. Support vectors and sample of fault of majority data were obtained from those granules. On the other hand, support vectors and sample of fault of minority data were obtained and the Synthetic Minority Over-sampling Technique (SMOTE) was adopted. Thus, two new kinds of samples were merged for SVM learning, so as to rebalance the training set and get a more reasonable classification of hyperplanes. The experimental results show that, in comparison with several other algorithms, the accuracy of the proposed algorithm decreases, but it significantly improves the g_means value and classification accuracy of positives and the effect is better on the imbalance rate of larger datasets.

Key words: unbalanced dataset, quotient space theory, Support Vector Machine (SVM), over-sampling, QMSVM algorithm