Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDs), which are then used to reason about the properties of these classifiers. Specifically, we present an algorithm for...

Studying materials informatics from a data mining perspective can be beneficial for manufacturing and other industrial engineering applications. Predictive data mining technique and machine learning algorithm are combined to design a knowledge discovery system for the selection of engineering materials that meet the design specifications. Predictive method-Naive Bayesian classifier and Machine ...

In this article, a contribution is made to information extraction and Bayesian network learning motivated by two practical information extraction tasks. It is shown that some information extraction tasks can be approached as a classification problem where the text is split in tokens and each token is assigned a class. Hidden Markov models are a popular formalism for this task, however they do n...

Naive Bayesian networks are often used for classification problems that involve variables of a continuous nature. Upon capturing such variables, their value ranges are modelled as finite sets of discrete values. While the output probabilities and conclusions established from a Bayesian network are dependent of the actual discretisations used for its variables, the effects of choosing alternativ...

In many applications, one can define a large set of features to support the classification task at hand. At test time, however, these become prohibitively expensive to evaluate, and only a small subset of features is used, often selected for their information-theoretic value. For threshold-based, Naive Bayes classifiers, recent work has suggested selecting features that maximize the expected ro...

Naive Bayes is a simple Bayesian classifier with strong independence assumptions among the attributes. This classifier, despite its strong independence assumptions, often performs well in practice. It is believed that relaxing the independence assumptions of a naive Bayes classifier may improve the classification accuracy of the resulting structure. While finding an optimal unconstrained Bayesi...

When modelling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous works have solved the problem by discretizing them with the consequent loss of information. Another common alternative assumes that the data are generated by a Gaussian distribution (parametric approach), such as conditional Gaussian networks, wit...

In this paper, we introduce a new learning algorithm for adaptive intrusion detection using boosting and naïve Bayesian classifier, which considers a series of classifiers and combines the votes of each individual classifier for classifying an unknown or known example. The proposed algorithm generates the probability set for each round using naïve Bayesian classifier and updates the weights of ...

The Naive Bayesian (NB) network classifier, a probabilistic model with a strong assumption of conditional independence among features, shows a surprisingly competitive prediction performance even when compared with some state-of-the-art classifiers. With a looser assumption of conditional independence, the Semi-Naive Beyesian (SNB) network classifier is superior to NB classifiers when features ...

Classification is an important task in data mining processes. In this work, the χ test is used to define the order of the variables of a dataset to be used in Bayesian classification tasks. Two Bayesian classifiers are used to verify the influence of the variables ordering in the classification rate. The first one is based on the K2 algorithm which has strong dependency upon the initial order o...