处理非平衡数据的粒度SVM学习方法

通过多维关联规则挖掘，将粒度计算（Granular Computing，GrC）和支持向量机（Support Vector Machine，SVM）有效融合，提出一种粒度支持向量机（Granular SVM，GSVM）学习方法，称为AR-GSVM。该方法用于非平衡数据处理时，不仅可以有效降低分类器的复杂性，而且本质上可以进行并行计算以提高学习效率，同时提高分类器的泛化能力。考虑到保持数据在原始空间和特征空间的分布一致性，在AR-GSVM的基础上又提出核空间上的粒度支持向量机学习方法，称为AR-KGSVM，该方法具有更好的泛化性能。通过在UCI数据集上的实验表明：AR-GSVM和AR-KGSVM的泛化能力优于一些常用非平衡数据处理的方法。

Granular support vector machine approach used for imbalanced data

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 learn-ing 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 datas-ets demonstrate that the generalization performances of AR-GSVM and AR-KGSVM are superior to some most common used methods in dealing with imbalanced datasets.