作　　者： ; ;

机构地区： 深圳信息职业技术学院

出　　处： 《深圳信息职业技术学院学报》 2010年第2期1-5,共5页

摘　　要： 为提高分类性能,提出了一种新的基于数据离散化和选择性集成的SVM集成学习算法。该算法采用粗糙集和布尔推理离散化方法处理数据集,构造有差异的个体SVM以提高集成学习的性能。在训练得到一批SVM之后,算法采用了选择性集成提高性能并减小集成规模。实验结果表明,所提算法能取得比传统集成学习方法Bagging和Adaboost更好的性能。 To improve the classification performance,a novel SVM ensemble learning algorithm based on discretization method and selective ensemble approach is proposed.This algorithm uses the discretized data sets obtained by the rough sets and boolean reasoning method to construct individual SVMs with good diversity,which can improve the performance of ensemble learning.After every SVM is trained separately,a selective approach to SVM ensemble is utilized to reduce the ensemble size and to improve its performance.Experimental results show that the presented algorithm achieves better performance than do the traditional ensemble learning methods such as Bagging and Adaboost.

关 键 词： 支持向量机 集成学习 选择性集成 离散化

领　　域： [自动化与计算机技术] [自动化与计算机技术]