The Naïve Bayesian Classifier and an Augmented Naïve Bayesian Classifier are applied to human classification tasks. The Naïve Bayesian Classifier is augmented with feature construction using a Galois lattice. The best features, measured on their withinand between-category overlap, are added to the category’s concept description. The results show that space efficient concept descriptions can pre...

On the basis of examining the existing restricted Bayesian network classifiers, a new Bayes-theorem-based and more strictly restricted Bayesian-network-based classification model DLBAN is proposed, which can be viewed as a double-level Bayesian network augmented naive Bayes classification. The experimental results show that the DLBAN classifier is better than the TAN classifier in the most cases.

In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted members...

In this paper we study the application of Bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of Bayesian networks as: Naive Bayes (NB), Tree Augmented Naive Bayes (TAN) and General Bayesian Networks (GBN), are applied to the classification of hyperspectral data. In addition, several Bayesian multi-net models: TAN multi-net, GBN multi-net ...

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 ...

Recent work in Bayesian classifiers has shown that a better and more flexible representation of domain knowledge results in better classification accuracy. In previous work [1], we have introduced a new type of Bayesian classifier called Case-Based Bayesian Network (CBBN) classifiers. We have shown that CBBNs can capture finer levels of semantics than possible in traditional Bayesian Networks (...

Our objective is to study the contribution of naive increased Bayesian networks in problems of image classification. The images used in this study represent the structure of a document containing text blocks and graphics. We proposed three variants of Bayesian networks. First naive Bayesian networks RN who, despite their simple structure and strong assumption on independence have given very goo...