Automated knowledge acquisition for PROSPECTOR-like expert systems
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Induction of ripple-down rules applied to modeling large databases
Journal of Intelligent Information Systems
Machine Learning
Fast Minimum Training Error Discretization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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"If-then" rules belong to the most popular formalism used to represent knowledge either obtained from human experts as in the case of expert systems or learned from data as in the case of machine learning and data mining. The most commonly used approach to learning decision rules is the set-covering approach, also called "separate and conquer". The other way to create decision rules is the compositional approach. The work reported in this paper fits into the latter approach. We will describe the KEX algorithm, its implementation within the LISp-Miner system, and results of empirical comparison of KEX with some other rule-learning algorithms implemented in the Weka system.