Abstract
Automatic knowledge acquisition for expert systems has attracted much attention. Several algorithms that infer concept descriptions from a given set of training examples have been developed to aid in this task, some of them elicit concepts from examples organized in data matrices. These algorithms infer from different training examples (or different matrices defined by a set of experts) slightly different concept descriptions. Herein we propose a method based on synthesis of judgements, fuzzy sets and classification methods that applied to a set of data matrices builds an agreed one that synthesises the information contained in the set of matrices. The method proposed can be applied to data matrices with attributes of several types: measure and ratio quantitative attributes, and ordered and nonordered qualitative ones. >
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