A decision generation algorithm based on granular computing

Min Yi Tsai, Ping Fang Chiang, Shao Jui Chen, Wei Jen Wang

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

Abstract

Granular computing aims to provide different views at different granules of data, and to derive knowledge from the process of data abstraction. In this paper, a decision-rule generation algorithm based on granular computing (DGAGC) is proposed. The DGAGC consists of two stages, the rule generation stage and the decision making stage. In the rule generation stage, the DGAGC employs a rule combination strategy and an alternative rule generation strategy to increase the accuracy of rules and the speed of generating rule in higher granularity. In the decision making stage, the DGAGC provides a novel rule-choosing strategy to use reasonable rules for decision making. By using this rule-choosing strategy, a better decision is made from many reasonable rules which are generated in stage one. The experimental results show that our algorithm works better than a prior similar study.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012
Pages475-480
Number of pages6
DOIs
StatePublished - 2012
Event2012 IEEE International Conference on Granular Computing, GrC 2012 - HangZhou, China
Duration: 11 Aug 201213 Aug 2012

Publication series

NameProceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012

Conference

Conference2012 IEEE International Conference on Granular Computing, GrC 2012
Country/TerritoryChina
CityHangZhou
Period11/08/1213/08/12

Keywords

  • granular computing
  • granule space
  • rule granule
  • rule-chossing
  • solution space

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