Adaptive Ripple Down Rules Method based on Minimum Description Length Principle Tetsuya Yoshida, Hiroshi Motoda, and Takashi Washio   View Paper (PDF)   Return to Main-Track Regular Papers Abstract When class distribution changes, some pieces of knowledge previously acquired become worthless, and the existence of such knowledge may hinder acquisition of new knowledge. This paper proposes an adaptive Ripple Down Rules (RDR) method based on the Minimum Description Length Principle aiming at knowledge acquisition in a dynamically changing enviromnent. To cope with the change of class distribution, knowledge deletion is carried out as well as knowledge acquisition so that useless knowledge is properly discarded. To cope with the change of the source of knowledge, RDR knowledge based systems can be constructed adaptively by acquiring knowledge from both domain experts and data. By incorporating inductive learning methods, knowledge acquision can be carried out even when only either data or experts are available by switching the source of knowledge from domain experts to data and vice versa at any time of knowledge acquisition. Since experts need not be available all the time, it contributes to reducing the cost of personnel expenses. Experiments were conducted by simulating the change of the source of knowledge and the change of class distribution using the datasets in UCI repository. The results are encouraging. DiSC'03 © 2003 Association for Computing Machinery