基于支持向量机挖掘不一致事例隐含的异常信息

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.

Mining Uncommon Information from Inconsistent Samples Based on Support Vector Machine

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.