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Volume 26 Issue 5
Aug.  2021
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Article Contents
ZHANG Dezheng, A Ziguli, FENG Honghai, YANG Bingru. Mining Uncommon Information from Inconsistent Samples Based on Support Vector Machine[J]. Chinese Journal of Engineering, 2004, 26(5): 564-568. doi: 10.13374/j.issn1001-053x.2004.05.027
Citation: ZHANG Dezheng, A Ziguli, FENG Honghai, YANG Bingru. Mining Uncommon Information from Inconsistent Samples Based on Support Vector Machine[J]. Chinese Journal of Engineering, 2004, 26(5): 564-568. doi: 10.13374/j.issn1001-053x.2004.05.027

Mining Uncommon Information from Inconsistent Samples Based on Support Vector Machine

doi: 10.13374/j.issn1001-053x.2004.05.027
  • Received Date: 2003-12-26
    Available Online: 2021-08-17
  • 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.

     

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      沈陽化工大學材料科學與工程學院 沈陽 110142

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