A Lattice machine approach to automated casebase design: marrying lazy and eager learning

Authors:
Hui Wang;Werner Dubitzky;Ivo Duntsch;David Bell
Affiliations:
School of Information and Software Engineering, University of Ulster, Newtownabbey, N.Ireland;School of Information and Software Engineering, University of Ulster, Newtownabbey, N.Ireland;School of Information and Software Engineering, University of Ulster, Newtownabbey, N.Ireland;School of Information and Software Engineering, University of Ulster, Newtownabbey, N.Ireland
Venue:
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Year:
1999

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Abstract

Case-based reasoning (CBR) is concerned with solving new problems by adapting solutions that worked for similar problems in the past. Years of experience in building and fielding CBR systems have shown that the "rase approach" is not free from problems. It has been realized that the knowledge engineering effort required for designing many real-world easebases can be prohibitively high. Based on the wide-spread use of databases and powerful machine learning methods, some CBR researchers have been investigating the possibility of designing casebases automatically. This paper proposes a flexible model for the automatic discovery of abstract cases from data bases based on the Lattice Machine. It also proposes an efficient and effective algorithm for retrieving such cases. Besides the known benefits associated with abstract cases, the main advantages of this approach are that the discovery process is fully automated (no knowledge engineering costs).