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

Abstract

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
Volume2
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

Keywords

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

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this