Title page for etd-0810110-175700


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URN etd-0810110-175700
Author Shian-Chi Tsai
Author's Email Address No Public.
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Department Electrical Engineering
Year 2009
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title A Mixed Approach for Multi-Label Document Classification
Date of Defense 2010-07-21
Page Count 54
Keyword
  • relevance score
  • information retrieval
  • Multi-Label document classification
  • fuzzy similarity measure
  • Abstract  Unlike single-label document classification, where each document exactly belongs to a single category, when the document is classified into two or more categories, known as multi-label file, how to classify such documents accurately has become a hot research topic in recent years. In this paper, we propose a algorithm named fuzzy similarity measure multi-label K nearest neighbors(FSMLKNN) which combines a fuzzy similarity measure with the multi-label K nearest neighbors(MLKNN) algorithm for multi-label document classification, the algorithm improved fuzzy similarity measure to calculate the similarity between a document and the center of cluster similarity, and proposed algorithm can significantly improve the performance and accuracy for multi-label document classification. In the experiment, we compare FSMLKNN and the existing classification methods, including decision tree C4.5, support vector machine(SVM) and MLKNN algorithm, the experimental results show that, FSMLKNN method is better than others.
    Advisory Committee
  • none - chair
  • none - co-chair
  • none - co-chair
  • none - co-chair
  • Shie-Jue Lee - advisor
  • Files
  • etd-0810110-175700.pdf
  • indicate access worldwide
    Date of Submission 2010-08-10

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