In recent years, machine learning research has started addressing a problem known as theory refinement. The goal of a theory refinement learner is to modify an incomplete or incorrect rule base, representing a domain theory, to make it consistent with a set of input training examples. This paper presents a major revision of the EITHER propositional theory refinement system. Two issues are discussed. First, we show how run time efficiency can be greatly improved by changing from a exhaustive scheme for computing repairs to an iterative greedy method. Second, we show how to extend EITHER to refine MofN rules. The resulting algorithm, Neither (New EITHER), is more than an order of magnitude faster and produces significantly more accurate results with theories that fit the MofN format. To demonstrate the advantages of NEITHER, we present experimental results from two real-world domains.

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Informatica, Vol. 17 (1993), pp. 387-397.

@Article{baffes:info93, title={Extending Theory Refinement to M-of-N Rules}, author={Paul T. Baffes and Raymond J. Mooney}, volume={17}, journal={Informatica}, key={NEITHER}, pages={387-397}, url="http://www.cs.utexas.edu/users/ai-lab?baffes:info93", year={1993} }