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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.

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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