Learning system fault diagnostic rules: A probabilistic inductive inference approach

Chun Chung Chan, J. Y. Ching, A. K.C. Wong

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In this paper, we present an inductive learning method that is effective for acquiring knowledge in the building of engineering expert systems. Our method is particularly suitable for the type of engineering applications that are classification in nature, such as system fault diagnosis, material and technology selection, reliability assessment, etc. The acquired classification knowledge is represented in easy-to-apply production rules that can be used to classify any new events. The performance of the method has been compared favorably elsewhere to ID3 for a number of application domains in terms of classification accuracy and computational efficiency. In this paper, we present an empirical comparison of our method to the AQ family of inductive learning systems using a typical engineering fault diagnosis task. We also suggest some reasons as to why our method performs better for some types of engineering applications, especially where the training data may be uncertain.
Original languageEnglish
Title of host publicationApplications of Artificial Intelligence in Engineering
PublisherPubl by Computational Mechanics Publ
Pages124-142
Number of pages19
ISBN (Print)1851667873
Publication statusPublished - 1 Dec 1992
Externally publishedYes
Event17th International Conference on Applications of Artificial Intelligence in Engineering - AIENG/92 - Waterloo, Ont, Canada
Duration: 1 Dec 1992 → …

Conference

Conference17th International Conference on Applications of Artificial Intelligence in Engineering - AIENG/92
Country/TerritoryCanada
CityWaterloo, Ont
Period1/12/92 → …

ASJC Scopus subject areas

  • Software

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