Hierarchical fuzzy-KNN networks for news documents categorization

Jung-Hsien Chiang, Yan Cheng Chen

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

In this paper, we present a document categorization method based on the hierarchical fuzzy networks. The proposed model employs the divide-and-conquer principle to resolve documents categorization problem based on a predefined hierarchical structure. The final classification framework can be interpreted as a hierarchical array of non-linear decision tree. Each node in the tree represents one filter. The fuzzy K-nearest-neighbor (KNN)-based filter decides that the unknown document belongs to the corresponding category or not. We use the Reuters-21578 news data set to evaluate the performance of the proposed method.

Original languageEnglish
Pages720-723
Number of pages4
Publication statusPublished - 2001 Dec 1
Event10th IEEE International Conference on Fuzzy Systems - Melbourne, Australia
Duration: 2001 Dec 22001 Dec 5

Other

Other10th IEEE International Conference on Fuzzy Systems
CountryAustralia
CityMelbourne
Period01-12-0201-12-05

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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  • Cite this

    Chiang, J-H., & Chen, Y. C. (2001). Hierarchical fuzzy-KNN networks for news documents categorization. 720-723. Paper presented at 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia.