Using Qualitative Models to Guide Inductive Learning

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ConferenceProceedings of the Tenth International Machine Learning Conference, June 27-29, 1993., Amherst, MA.
AbstractThis paper presents a method for using qualitative models to guide inductive learning. Our objectives are to induce rules which are not only accurate but also explainable with respect to the qualitative model, and to reduce learning time by exploiting domain knowledge in the learning process. Such explainability is essential both for practical application of inductive technology, and for integrating the results of learning back into an existing knowledge-base. We apply this method to two process control problems, a water tank network and an ore grinding process used in the mining industry. Surprisingly, in addition to achieving explainability the classificational accuracy of the induced rules is also increased. We show how the value of the qualitative models can be quantified in terms of their equivalence to additional training examples,and finally discuss possible extensions.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number37079
NPARC number5763468
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Record identifier54c7137b-9f9d-45a0-9cc9-96ecb7c0d805
Record created2009-03-29
Record modified2016-05-09
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