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A machine learning approach to the automatic synthesis of mechanistic knowledge for engineering decision-making

Published online by Cambridge University Press:  27 February 2009

Stephen C.-Y. Lu and
Kaihu Chen
Stephen C.-Y. Lu
Affiliation:
Knowledge-Based Engineering Systems Research Laboratory, Department of Mechanical and Industrial Engineering, University of Illinois at Urbana–Champaign, Urbana, IL 61801, U.S.A.
Kaihu Chen
Affiliation:
Knowledge-Based Engineering Systems Research Laboratory, Department of Mechanical and Industrial Engineering, University of Illinois at Urbana–Champaign, Urbana, IL 61801, U.S.A.
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Abstract

Inductive learning is proposed as a tool for synthesizing domain knowledge from data generated by a model-based simulator. In order to use an inductive engine to generate decision rules, the pre-classification process becomes more complicated in the presence of multiple competing objectives. Instead of relying on a domain expert to perform this pre-classification task, a clustering algorithm is used to eliminate human biases involved in the selection of a classification function for pre-classification. It is shown that the use of a clustering algorithm for pre-classification not only further automates the process of knowledge by synthesizing, but also improves the quality of the rules generated by the inductive engine.

Information

Type
Research Article
Information
AI EDAM , Volume 1 , Issue 2 , May 1987, pp. 109 - 118
Copyright
Copyright © Cambridge University Press 1987

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