Inferring decision trees using the minimum description length principle
Information and Computation
A philosophical basis for knowledge acquisition
Knowledge Acquisition
Proceedings of the sixth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Optimal network construction by minimum description length
Neural Computation
Induction of ripple-down rules applied to modeling large databases
Journal of Intelligent Information Systems
Concept Formation and Knowledge Revision
Concept Formation and Knowledge Revision
A description length-based decision criterion for default knowledge in the ripple down rules method
Knowledge and Information Systems
Knowledge Acquisition and Machine Learning
Knowledge Acquisition and Machine Learning
Validating knowledge acquisition: multiple classification ripple-down rules
Validating knowledge acquisition: multiple classification ripple-down rules
Two decades of ripple down rules research
The Knowledge Engineering Review
Incremental knowledge acquisition using generalised RDR for soccer simulation
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Detection of CAN by ensemble classifiers based on ripple down rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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A knowledge acquisition method Ripple Down Rules (RDR) can directly acquire and encode knowledge from human experts. It is an incremental acquisition method and each new piece of knowledge is added as an exception to the existing knowledge base. Past researches on RDR method assume that the problem domain is stable. This is not the case in reality, especially when an environment changes. Things change over time. This paper proposes an adaptive Ripple Down Rules method based on the Minimum Description Length Principle aiming at knowledge acquisition in a dynamically changing environment. We consider both the change in class distribution on a domain and the change in knowledge source as typical changes in the environment. When class distribution changes, some pieces of knowledge previously acquired become worthless, and the existence of such knowledge may hinder acquisition of new knowledge. In our approach knowledge deletion is carried out as well as knowledge acquisition so that useless knowledge is properly discarded. To cope with the change in knowledge source, RDR knowledge based systems can be constructed adaptively by acquiring knowledge from both domain experts and data. By incorporating inductive learning methods, knowledge can be acquired (learned) even when only either data or experts are available by switching the knowledge source 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 in knowledge source and the change in class distribution using the datasets in UCI repository. The results show that it is worth following this path.