This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion)...

In this paper, we employed Naïve Bayes, Augmented Naïve Bayes, Tree Augmented Naïve Bayes, Sons & Spouses, Markov Blanket, Augmented Markov Blanket, Semi Supervised and Bayesian network techniques to rank web services. The Bayesian Network is demonstrated on a dataset taken from literature. The dataset consists of 364 web services whose quality is described by 9 attributes. Here, the attributes...

Article history: Received 19 March 2008 Received in revised form 8 August 2008 Accepted 5 January 2009 Available online 13 January 2009

We present a new method to approximate posterior probabilities of Bayesian Network using Deep Neural Network. Experiment results on several public Bayesian Network datasets shows that Deep Neural Network is capable of learning joint probability distribution of Bayesian Network by learning from a few observation and posterior probability distribution pairs with high accuracy. Compared with tradi...

We study cross-validation as a scoring criterion for learning dynamic Bayesian network models that generalize well. We argue that cross-validation is more suitable than the Bayesian scoring criterion for one of the most common interpretations of generalization. We confirm this by carrying out an experimental comparison of cross-validation and the Bayesian scoring criterion, as implemented by th...

Collaboration readiness depends on “hard” factors such as competency fitness or technological preparedness, but also on several other factors of a “soft” nature such as organization’s character, willingness to collaborate, or affectivity / empathy relationships. A modeling approach to assess how prepared is an enterprise to join a collaborative network is proposed. The approach is based on a no...

The aim of this paper is to compare Bayesian network classifiers to the k-NN classifier based on a subset of features. This subset is established by means of sequential feature selection methods. Experimental results show that Bayesian network classifiers more often achieve a better classification rate on different data sets than selective k-NN classifiers. The k-NN classifier performs well in ...