This article describes a comprehensive approach to automatic theory revision. Given an imperfect theory, the approach combines explanation attempts for incorrectly classified examples in order to identify the failing portions of the theory. For each theory fault, correlated subsets of the examples are used to inductively generate a correction. Because the corrections are focused, they tend to preserve the structure of the original theory. Because the system starts with an approximate domain theory, in general fewer training examples are required to attain a given level of performance (classification accuracy) compared to a purely empirical system. The approach applies to classification systems employing a propositional Horn-clause theory. The system has been tested in a variety of application domains, and results are presented for problems in the domains of molecular biology and plant disease diagnosis.

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Artificial Intelligence (1994), pp. 311-344.

@Article{ourston:ai94, title={Theory Refinement Combining Analytical and Empirical Methods}, author={Dirk Ourston and Raymond J. Mooney}, journal={Artificial Intelligence}, pages={311-344}, url="http://www.cs.utexas.edu/users/ai-lab?ourston:ai94", year={1994} }