Abstract: A decision structure is a simple and powerful tool for organizing a decision process. It differs from a conventional decision tree in that its nodes are assigned tests that can be functions of the attributes, rather than single attributes; the branches stemming from a node can be assigned a subset of attribute values rather than a single attribute value (test outcome); and the leaves can be assigned one or more alternative decisions. We describe a methodology for learning decision structures from declarative knowledge expressed in the form of decision rules. The decision rules are generated by an expert, or by an AQ-type inductive learning program (with or without constructive induction). From a given set of rules, one can generate many different decision structures. The proposed methodology generates the one that is most suitable for the given decision-making situation, according to a multicriterion evaluation function. Experiments with a program implementing the proposed methodology have demonstrated its many useful features.
Publication Year: 1997
Publication Date: 1997-01-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 13
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