Abstract:
Research on a new system implementing the AQ learning methodology, called AQ20, is briefly described, and illustrated by initial results from an experimental version. Like its predecessors, AQ20 is a multi-purpose learning system for inducing general concepts descriptions from concept examples and counter-examples. AQ20 is viewed as a natural induction system because it aims at producing descriptions that are not only accurate but also easy to understand and interpret. This feature is achieved by representing descriptions in the form of attributional rulesets that have a higher representation power than decision trees or conventional decision rules. Among new features implemented in AQ20 are the ability to handle continuous variables without prior discretization, to control the degree of generality of rules by a continuous parameter, and to generate more than one rule from a star. Initial experimental results from applying AQ20 to selected problems in the UCI repository demonstrate a high utility of the new system.
