Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
C4.5: programs for machine learning
C4.5: programs for machine learning
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Machine Learning
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Defining explanation in probabilistic systems
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop
Engineering Applications of Artificial Intelligence
Feature selection for Bayesian network classifiers using the MDL-FS score
International Journal of Approximate Reasoning
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A Bayesian network (BN) is a formalism for representing and reasoning about uncertain domains. In BNs the knowledge is represented by a combination of a graph-based structure and probability theory. A particular type of BN known as Bayesian Classifier (BC) aims at classifying a given instance into a discrete class. BCs have been intensively used for knowledge modeling in many different applications and have been the focus of many works related to data mining. Data mining tasks are usually applied to real domains having large number of variables. In such domains, the classifiers tend to be large and complex and consequently are not so easily understood by human beings. This paper proposes an approach for promoting the understandability of the knowledge represented by a BC, by translating it into a more convenient and easily understandable form of representation, that of classification rules. The proposed method named BayesRule uses the concept of Markov-Blanket to obtain a reduced set of rules in relation to both, the number of rules and the number of conditions in the antecedent of a rule. Experiments using seven knowledge domains show that the reduced set of rules extracted from a BC can be smaller and still maintain the BC classification accuracy.