RM and RDM, a preliminary evaluation of two prudent RDR techniques

Authors:
Omaru Maruatona;Peter Vamplew;Richard Dazeley
Affiliations:
Internet Commerce Security Laboratory, University of Ballarat, Ballarat, Australia;Internet Commerce Security Laboratory, University of Ballarat, Ballarat, Australia;Internet Commerce Security Laboratory, University of Ballarat, Ballarat, Australia
Venue:
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Year:
2012

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Abstract

Rated Multiple Classification Ripple Down Rules (RM) and Ripple Down Models (RDM) are two of the successful prudent RDR approaches published. To date, there has not been a published, dedicated comparison of the two. This paper presents a systematic preliminary evaluation and analysis of the two techniques. The tests and results reported in this paper are the first phase of direct evaluations of RM and RDM against each other.