On Learning Decision Structures

A decision structure is a simple and powerful tool for organizing decision processes. 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. This paper describes 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 cost function. Experiments with a program implementing the methodology have indicated many advantages of the proposed methodology.