ENHANCED ALGORITHM PERFORMANCE FOR CLASSIFICATION BASED ON HYPER SURFACE USING BAGGING AND ADABOOST

摘要：

To improve the generality ability of Hyper Surface Classification (HSC), Bagging and Ada Boost ensemble learning methods arc proposed in this paper.HSC is a covering learning algorithm, in which a model of hyper surface is obtained by adaptively dividing the sample space and then the hyper surface is directly used to classify large database based on Jordan Curve Theorem in Topology.Experiments results confirm that Bagging and Ada Boost can improve the generality ability of Hyper Surface Classification (HSC) in general.However, its behavior is subject to the characteristics of Minimal Consistent Subset for a disjoint Cover set (MCSC).Usually the accuracy of Bagging and AdaBoost can not exceed the accuracy predicted by MCSC.So MCSC is the backstage manipulator of generalization ability.

关键词： Minimal consistent subset Hyper surface cassification Bagging AdaBoost

作者: QING HE FU-ZHEN ZHUANG XIU-RONG ZHAO ZHONG-ZHI SHI

作者单位: The Key Laboratory of Intelligent Information Processing, Department of Intelligence Software, Insti The Key Laboratory of Intelligent Information Processing, Department of Intelligence Software, Insti

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 3624-3629

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）