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Bayesian Classifiers Applied to the Tennessee Eastman Process
Author(s) -
Santos Edimilson Batista,
Ebecken Nelson F. F.,
Hruschka Estevam R.,
Elkamel Ali,
Madhuranthakam Chandra M. R.
Publication year - 2014
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.12112
Subject(s) - artificial intelligence , machine learning , markov blanket , decision tree , computer science , bayesian network , naive bayes classifier , classifier (uml) , bayesian probability , probabilistic classification , data mining , pattern recognition (psychology) , markov chain , markov model , support vector machine , markov property
Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.