A topic-specific data filtering framework based on rough set theory

Hong Guo, Yunda Cao, Song Guo

Research output: Journal article publicationConference articleAcademic researchpeer-review


With the tremendous growth in the volume of text documents available on the Internet and digital libraries, accurate specific topic text filtering is needed. In this paper we propose a Rough Set aided method to reduce the dimensionality of feature vectors. In order to extract accurate features, we also provide a novel filtering technique called twice-filtering to treat with two different feature sets; "Inter-Keywords" and "Intra-Keyword", A\ simple application of E-mail filtering system based on our topic-specific filtering technology shows that with the incorporation of variant weighting methods and more accurate features extracted, our filtering algorithm can speedup the filtering operation with a high precision and recall.
Original languageEnglish
Pages (from-to)1095-1098
Number of pages4
JournalCanadian Conference on Electrical and Computer Engineering
Publication statusPublished - 1 Oct 2003
Externally publishedYes
EventCCECE 2003 Canadian Conference on Electrical and Computer Engineering: Toward a Caring and Humane Technology - Montreal, Canada
Duration: 4 May 20037 May 2003


  • DF
  • Document Filtering
  • Precision
  • Recall
  • Rough Set
  • TF-IDF

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this