Text classification algorithm study based on rough set theory

Xun Lin, Zhishu Li, Yong Zhou, Yuan Xue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Text Classification is an important research area in Chinese information processing, whose goal is on the base of analyzing the text content to give the allocation of one or more of the text to more appropriate classes to enhance the text retrieval, storage, applications such as processing efficiency.In this paper, text dataset is transformed to information system without attribute of decision making and the core content of attribute reduction has been applied to text classification. Experiment shows that the precision rate and recall rate are enhanced in this method; furthermore, it does not require any a priori information .In this paper, The first Determination of the text vector, The second generates Text set information systems, The third Attribute value discretization.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 International Forum on Information Technology and Applications, IFITA 2010
Pages117-120
Number of pages4
DOIs
StatePublished - 2010
Event2010 International Forum on Information Technology and Applications, IFITA 2010 - Kunming, China
Duration: Jul 16 2010Jul 18 2010

Publication series

NameProceedings - 2010 International Forum on Information Technology and Applications, IFITA 2010
Volume1

Other

Other2010 International Forum on Information Technology and Applications, IFITA 2010
CountryChina
CityKunming
Period7/16/107/18/10

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Text classification algorithm study based on rough set theory'. Together they form a unique fingerprint.

Cite this