To overcome the defect that K2 algorithm requires the suitable order of nodes in advance while dealing with the structure learning of Bayesian Network Classifier (BNC), the algorithm GA-K2 is proposed which introduces the integer coding genetic algorithm based on selective ensemble concept to K2. It provides the guarantee of getting the best order of nodes and the convergence of Bayesian network structure for K2 in global optimization.

It introduces the building of experiment platform MBNC for Bayesian Classifiers using Matlab based on BNT, including the system structure and the main function of MBNC, the Classifiers built on MBNC: the Nave Bayesian Classifier NBC, the Tree Augmented Nave Bayesian Classifier TANC based on Mutual Information and Conditional Mutual Information, Bayesian Network Classifier BNC based on K2 and GS algorithm.

It introduces the building of experiment platform MBNC for Bayesian Classifiers using Matlab based on BNT, including the system structure and the main function of MBNC, the Classifiers built on MBNC: the Nave Bayesian Classifier NBC, the Tree Augmented Nave Bayesian Classifier TANC based on Mutual Information and Conditional Mutual Information, Bayesian Network Classifier BNC based on K2 and GS algorithm.

1. In our research, the Bayesian network classifier is firstly used in the transformer fault diagnosis. We develops three classifier models: NB, TAN and BAN classifier, which hold the high accuracy when there is not much information lost for Bayesian classifier is able to handle incomplete information, in transformer fault diagnosis.

Secondly we introduce a few Bayesian classification models, such as Naive Bayesian Classifier, Bayesian Network Classifier and Incremental Bayesian Classifier.

The experiments show that the selective unrestricted Bayesian network classifier outperforms the na?ve Bayes and the tree-augmented na?ve Bayes decision rules concerning the classification rate.

The experiments show that the selective unrestricted Bayesian network classifier outperforms the na?ve Bayes and the tree-augmented na?ve Bayes decision rules concerning the classification rate.

In this paper we present the Dempster-Shafer theory as a framework within which the results of a Bayesian network classifier and a fuzzy logic-based classifier are combined to produce a better final classification.

The simplest restricted Bayesian network classifier is the naive Bayesian classifier.

The graph visualization option only appears if a Bayesian network classifier has been built.

Bayesian network classifier has good capability to handle problems with high uncertainty, and is fit for customer modeling in CRM. Based on the analysis of naive Bayesian classifier and general Bayesian network classifier, augmented naive Bayesian classifier and Bayesian multi-net classifier were introduced, and the detailed algorithm of the latter one was described. We applied Bayesian multi-net classifier in customer modeling of telecommunications...

Bayesian network classifier has good capability to handle problems with high uncertainty, and is fit for customer modeling in CRM. Based on the analysis of naive Bayesian classifier and general Bayesian network classifier, augmented naive Bayesian classifier and Bayesian multi-net classifier were introduced, and the detailed algorithm of the latter one was described. We applied Bayesian multi-net classifier in customer modeling of telecommunications CRM, and got effective results.

The classification is an important and basic ability for human obtained by learning. It has been considered as a key research area in machine learning, pattern recognition and data mining. It is proved that a Bayesian network classifier restricted by class variable is optimal under zero-one loss rate. The most important problem of setting up the classifier is to learning the structure of attributes Bayesian network restricted by class variable. In this paper, the method of learning the...

The classification is an important and basic ability for human obtained by learning. It has been considered as a key research area in machine learning, pattern recognition and data mining. It is proved that a Bayesian network classifier restricted by class variable is optimal under zero-one loss rate. The most important problem of setting up the classifier is to learning the structure of attributes Bayesian network restricted by class variable. In this paper, the method of learning the structure of attributes Bayesian network is developed. In learning the method of orienting edges based on the causal semanitics of an arc's direction is used. The method is combined with that of orienting edges based on collider identification to make superfluous arcs disposal after orienting edges. The problems brought by checking superfluous edges before orienting edges are avoided. The efficiency and veracity of learning Bayesian network structure is markedly improved. A contrast experiment is conducted by simulation and the results are analyzed.

To test and evaluate the performance of Bayesian Classifier, it is absolutely necessary to carry through contrastive experiment using different data sets. Current packages for Bayesian Classifier experiment are designed for certain purposes, so that it can't satisfy the needs of different research. It introduces the building of experiment platform MBNC for Bayesian Classifiers using Matlab based on BNT, including the system structure and the main function of MBNC, the Classifiers...

To test and evaluate the performance of Bayesian Classifier, it is absolutely necessary to carry through contrastive experiment using different data sets. Current packages for Bayesian Classifier experiment are designed for certain purposes, so that it can't satisfy the needs of different research. It introduces the building of experiment platform MBNC for Bayesian Classifiers using Matlab based on BNT, including the system structure and the main function of MBNC, the Classifiers built on MBNC: the Nave Bayesian Classifier NBC, the Tree Augmented Nave Bayesian Classifier TANC based on Mutual Information and Conditional Mutual Information, Bayesian Network Classifier BNC based on K2 and GS algorithm. MBNC is tested by standard data set from UCI and the results show that the performance of Bayesian classifiers built on MBNC are preceded similar works and the quantity of programming much less than that using current packages, which indicates that the platform works correctly, effectively and stably. Now the experiments for optimizing Bayesian Classifiers and the study of dealing with missing data are carried through on MBNC.