A diagnostic system based on a multi-decision approximate rules model

Ray R. Hashemi, Fred Choobineh, John Talburt, William Slikker, Merle G. Paule

Research output: Contribution to book or proceedingConference articlepeer-review

1 Scopus citations

Abstract

Modified Rough Sets (MRS)-based diagnostic systems eliminated many of the limitations of Rough Sets (RS)- based, statistical-based, and decision tree-based systems and because of that, they have a better performance. In contrast with the other systems, MRS-based diagnostic systems have potential to handle Multi-Decision Approximate (MDA) rules. In this paper, we (1) develop a diagnostic model based on MDA rules and (2) evaluate the classification power of the model.

Original languageEnglish
Title of host publicationProceedings of the 1997 ACM Symposium on Applied Computing, SAC 1997
PublisherAssociation for Computing Machinery
Pages20-24
Number of pages5
ISBN (Print)0897918509, 9780897918503
DOIs
StatePublished - 1997
Event1997 ACM Symposium on Applied Computing, SAC 1997 - San Jose, CA, United States
Duration: Feb 28 1997Mar 1 1997

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference1997 ACM Symposium on Applied Computing, SAC 1997
Country/TerritoryUnited States
CitySan Jose, CA
Period02/28/9703/1/97

Scopus Subject Areas

  • Software

Keywords

  • Approximate rule
  • Diagnostic system
  • Modified rough sets
  • Multi-decision approximate rule
  • Rough sets

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