基于支持向量机挖掘不一致事例隐含的异常信息  

Mining Uncommon Information from Inconsistent Samples Based on Support Vector Machine

作　　者：张德政[1] 阿孜古丽[1] 冯洪海[1] 杨炳儒[1] 

机构地区：[1]北京科技大学信息工程学院,北京100083

出　　处：《北京科技大学学报》2004年第5期564-568,共5页Journal of University of Science and Technology Beijing

基　　金：科技部推广应用项目(No.EC100000);校科研启动基金资助

摘　　要：基于支持向量机,提出一种挖掘粗集信息表中不一致事例背后隐藏某种有价值信息的算法,即不一致是由于错误引起,还是由于误差引起,抑或是由于缺少属性引起,并提出一些排除不一致的方案和算法.In current researches of knowledge discovery, inconsistent examples in a decision table are not be analyzed. It is just the place that contradictions would hide interesting and valuable information. An algorithm based on the support vector machine is proposed to mine kinds of information which hide in inconsistent examples, i.e., to decide whether inconsistency is caused by mistake, the error between a computed or measured value and a true or theoretically correct value, or missing attributes. Some methods and algorithms which eliminate the inconsistency are presented.

关 键 词：知识发现 粗糙集 支持向量机 不一致 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]