Ripple-Down Rules (RDR) has been successfully used to implement incremental knowledge acquisition systems. Its success largely depends on the organisation of rules, and less attention has been paid to its knowledge repre- sentation scheme. Most RDR used standard production rules and exception rules. With sequential processing, RDR acquires exception rules for a particular rule only after the rule wrongly classifies cases. We propose censored produc- tion rules (CPR), to be used for acquiring exceptions when a new rule is created using censor conditions. This approach is useful when we have a large number of validation cases at hand. We discuss inference and knowledge acquisition al- gorithms and related issues. The approach can be combined with machine learn- ing techniques to acquire censor conditions.
History
Publication title
Proceedings of the Knowledge Management and Acquisition for Intelligent Systems (PKAW 2012)
Editors
D Richards and BH Kang
Pagination
175-187
ISBN
978-3-642-32540-3
Department/School
School of Information and Communication Technology
Publisher
Springer- Verlag
Place of publication
Berlin Heidelberg
Event title
12th Pacific Rim Knowledge Acquisition Workshop, PKAW 2012
Event Venue
Kuching, Malaysia
Date of Event (Start Date)
2012-09-05
Date of Event (End Date)
2012-09-06
Rights statement
Copyright 2012 Springer
Repository Status
Restricted
Socio-economic Objectives
Information systems, technologies and services not elsewhere classified