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Incremental Rule-based Learning

Higgins, Charles M. and Goodman, Rodney M. (1991) Incremental Rule-based Learning. In: Proceedings. 1991 IEEE International Symposium on Information Theory. IEEE , Piscataway, NJ, p. 288. ISBN 0780300564.

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In a system which learns to predict the value of an output variable given one or more input variables by looking at a set of examples, a rule-based knowledge representation provides not only a natural method of constructing a classifier, but also a human-readable explanation of what has been learned. Consider a rule of the form if y then x where y is a conjunction of values of input variables and x is a value of the output variable. The number of input variables in y is called the order of the rule. In previous work, a measure of the information content or "value" of such a rule has been developed (the J-measure. It has been shown in [3] that a classifier can be built from the rules obtained by a constrained search of all possible rules which performs comparably with other classifiers.

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Additional Information:© 1991 IEEE. This work is supported in part by the Army Research Office under contract number DAAL03-89-K-0126 and in part by DARPA under contract number AFOSR-90-0199.
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Army Research Office (ARO)DAAL03-89-K-0126
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Air Force Office of Scientific Research (AFOSR)AFOSR-90-0199
Record Number:CaltechAUTHORS:20190314-142001533
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Official Citation:C. M. Higgins and R. M. Goodman, "Incremental Rule-based Learning," Proceedings. 1991 IEEE International Symposium on Information Theory, Budapest, Hungary, 1991, pp. 288-288. doi: 10.1109/ISIT.1991.695344
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93842
Deposited By: George Porter
Deposited On:14 Mar 2019 22:38
Last Modified:16 Nov 2021 17:01

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