Tolerance relation based granular space

  • Zheng Zheng*
  • , Hong Hu
  • , Zhongzhi Shi
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Granular computing as an enabling technology and as such it cuts across a broad spectrum of disciplines and becomes important to many areas of applications. In this paper, the notions of tolerance relation based information granular space are introduced and formalized mathematically. It is a uniform model to study problems in model recognition and machine learning. The key strength of the model is the capability of granulating knowledge in both consecutive and discrete attribute space based on tolerance relation. Such capability is reestablished in granulation and an application in information classification is illustrated. Simulation results show the model is effective and efficient.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages682-691
Number of pages10
DOIs
StatePublished - 2005
Externally publishedYes
Event10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005 - Regina, Canada
Duration: 31 Aug 20053 Sep 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3641 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005
Country/TerritoryCanada
CityRegina
Period31/08/053/09/05

Fingerprint

Dive into the research topics of 'Tolerance relation based granular space'. Together they form a unique fingerprint.

Cite this