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《Journal of University of Science and Technology Beijing》 2004-05
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Mining Uncommon Information from Inconsistent Samples Based on Support Vector Machine

ZHANG Dezheng, AZIGULI, FENG Honghai, YANG Bingru Information Engineering School, University of Science and Technolgy Beijing, Beijing 100083, China  
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. A support vector machine based algorithm 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.
【Fund】: 科技部推广应用项目(No.EC100000);; 校科研启动基金资助
【CateGory Index】: TP18
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