Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Learning Bayesian Networks
On the incompatibility of faithfulness and monotone DAG faithfulness
Artificial Intelligence
Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome
Expert Systems with Applications: An International Journal
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A Bayesian Network (BN) is a multivariate joint probability distribution graphical representation that can be induced from data. The induction of a BN is a NP problem. Two main approaches can be used for inducing a BN from data, namely, Conditional Independence (CI) and the Heuristic Search (HS) based algorithms. When a BN is induced for classification purposes (Bayesian Classifier - BC), it is possible to impose some specific constraints aiming at an increase in computational efficiency. In this paper a new CI based algorithm (MarkovPC) to induce BCs from data is proposed. MarkovPC uses the Markov Blanket concept in order to impose some constraints and optimize the traditional PC algorithm. Experiments performed with ALARM BN, as well as other UCI and artificial domains revealed that MarkovPC tends to execute fewer comparisons than the traditional PC. The experiments also show that the MarkovPC produces competitive classification rates when compared with both, PC and Naïve Bayes.