language: en

Rule Combination in Inductive Learning

Luis Torgo
LIACC
R.Campo Alegre, 823 - 2. 4100 PORTO PORTUGAL

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email : ltorgo@ciup1.ncc.up.pt

Abstract. This paper describes the work on methods for combining rules obtained by machine learning systems. Three methods for obtaining the classification of examples with those rules are compared. The advantages and disadvantages of each method are discussed and the results obtained on three real world domains are commented. The methods compared are: selection of the best rule; PROSPECTOR-like probabilistic approximation for rule combination; and MYCIN-like approximation. Results show significant differences between methods indicating that the problemsolving strategy is important for accuracy of learning systems.

1 Introduction