A Maximum Contribution Method for Classification Based on Information Theory

摘要：

Inductive learning for classification based oninformation theory is one of the important topics indata mining.We here propose an Maximumcontribution method for classification based oninformation theory.According to the theory of channeltransmission in information theory,the definitioncontribution is developed based on probabilitydistribution of classified space,probability transfermatrices of classified space and feature space andmutual information,then entities is classified by theMaximum contribution method Finally the empiricaltest and analyses prove the feasibility of the method.

作者: Lin Keming Xue Yongsheng Wen Juan

作者单位: Department of Mathematics and Computer Science,SanmingUniversity,Sanming,365004,China Department of Computer Science,Xiamen University,Xiamen,361005,China Department of Planning Stat,Xiamen University,Xiamen,361005,China

会议类型: 国际会议

会议名称: 2008 3rd International Conference on Intelligent System and Knowledge Engineering(第三届智能系统与知识工程国际会议)(ISKE 2008)

会议地点: 厦门

会议语种:英文

页码: 345-350

在线出版日期: 2008-11-17（万方平台首次上网日期，不代表论文的发表时间）