Multi-label Text Categorization Using K-Nearest Neighbor Approach with M-Similarity

  • Yi Feng
  • Zhaohui Wu
  • Zhongmei Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3772)


Due to the ubiquity of textual information nowadays and the multi-topic nature of text, it is of great necessity to explore multi-label text categorization problem. Traditional methods based on vector-space-model text representation suffer the losing of word order information. In this paper, texts are considered as symbol sequences. A multi-label lazy learning approach named kNN-M is proposed, which is derived from traditional k-nearest neighbor (kNN) method. The flexible order-semisensitive measure, M-Similarity, which enables the usage of sequence information in text by swap-allowed dynamic block matching, is applied to evaluate the closeness of texts on finding k-nearest neighbors in kNN-M. Experiments on real-world OHSUMED datasets illustrate that our approach outperforms existing ones considerably, showing the power of considering both term co-occurrence and order on text categorization tasks.


Text Categorization String Match String Kernel Thresholding Strategy Text Categorization Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yi Feng
    • 1
  • Zhaohui Wu
    • 1
  • Zhongmei Zhou
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouP.R. China

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