基于动态粗糙集约简的选择性支持向量机集成

支持向量机集成是提高支持向量机泛化性能的有效手段,个体支持向量机的泛化能力及其之间的差异性是影响集成性能的关键因素。为了进一步提升支持向量机整体泛化性能,提出利用动态粗糙集的选择性支持向量机集成算法。首先在利用Boosting算法对样本进行扰动基础上,采用遗传算法改进的粗糙集与重采样技术相结合的动态约简算法进行特征扰动,获得稳定、泛化能力较强的属性约简集,继而生成差异性较大的个体学习器;然后利用模糊核聚类根据个体学习器在验证集上的泛化误差来选择最优个体;并用支持向量机算法对最优个体进行非线性集成。通过在UCI数据集进行仿真,结果表明算法能明显提高支持向量机的泛化性能,具有较低的时、空复杂性,是一种高效、稳定的集成方法。

Selective SVM Ensemble Based on Dynamic Rough Set

Ensemble is an effective method to improve generalization performance of SVM.Individual SVM’s accuracy and the difference between SVMs are two key factors to affect the generalization performances.Selective SVM ensemble based on dynamic rough set was presented to improve the generalization ability of SVM.First,the training samples were disturbed by using conventional Boosting algorithm.A dynamic reduction technology,which integrates genetic algorithm and resample method,was used to acquire the reducted sets that have stable and good generalization ability.Best individual was selected according to generalization error of SVM based on the validate set based on KFCM.Finally,the selected members were ensembled nonlinearly by SVM.The experiments show that the algorithm has higher generalization performance and lower time and space complexity.It is a higher effect ensemble algorithm.