关键词: 支持向量机, 粒度计算, 粒度支持向量机, 关联规则, 非平衡数据

Abstract: Through the mining of multi-dimension association rules，Granular Computing（GrC） and Support Vector Machine（SVM） are efficiently amalgamated，and a Granular Support Vector Machine（GSVM） learning approach is proposed，namely AR-GSVM.For imbalanced datasets，AR-GSVM can not only reduce the complexity of the classifier，but also improve learning efficiency and generalization performance.Considering the data distribution consistence in the input space and kennel space，another granular SVM model on kennel space based on AR-GSVM is proposed，which is named as AR-KGSVM.AR-KGSVM can obtain better generalization performance comparing with AR-GSVM.The experimental results on UCI datasets demonstrate that the generalization performances of AR-GSVM and AR-KGSVM are superior to some most common used methods in dealing with imbalanced datasets.

Key words: support vector machine, granular computing, granular support vector machine, association rules, imbalanced data