C4.5: programs for machine learning
C4.5: programs for machine learning
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Learning Problem-Oriented Decision Structures from Decision Rule: The AQDT-2 System
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
An empirical comparison of three inference methods
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Human Problem Solving
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
B2R: an algorithm for converting Bayesian networks to sets of rules
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
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Nowadays, several knowledge representation methods are being used in knowledge based systems, machine learning, and data mining. Among them are decision rules and Bayesian networks. Both methods have specific advantages and disadvantages. A conversion method would allow to exploit advantages of both techniques. In this paper an algorithm that converts Naive Bayes models with multi-valued attribute domains into sets of rules is proposed. Experimental results show that it is possible to generate rule-based classifiers, which have relatively high accuracy and are simpler than original models.