Inferring decision trees using the minimum description length principle
Information and Computation
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
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
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
RDRCE: combining machine learning and knowledge acquisition
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Hi-index | 0.00 |
A Knowledge Acquisition method "Ripple Down Rules" 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. This knowledge base takes the form of a binary tree. There is another type of knowledge acquisition method that learns directly from data. Induction of decision tree is one such representative example. Noting that more data are stored in the database in this digital era, use of both expertise of humans and these stored data becomes even more important. In this paper, we attempt to integrate inductive learning and knowledge acquisition. We show that using the minimum description length principle, the knowledge base of Ripple Down Rules is automatically and incrementally constructed from data and thus, making it possible to switch between manual acquisition by a human expert and automatic induction from data at any point of knowledge acquisition. Experiments are carefully designed and tested to verify that the proposed method indeed works for many data sets having different natures.