Using data mining techniques in intrusion detection systems is common for the classification of the network events as either normal events or attack events. Naïve Bayes (NB) method is a simple, efficient and popular data mining method that is built on conditional independence of attributes assumption. Hidden Naïve Bayes (HNB) is an extended form of NB that keeps the NB's simplicity and efficien...

Partially specified data are commonplace in many practical applications of machine learning where different instances are described at different levels of precision relative to an attribute value taxonomy (AVT). This paper describes AVT-NBL – a variant of the Naïve Bayes Learning algorithm that effectively exploits user-supplied attribute value taxonomies to construct compact and accurate Naïve...

This paper proposes a new approach based on augmented naive Bayes for image classification. Initially, each image is cutting in a whole of blocks. For each block, we compute a vector of descriptors. Then, we propose to carry out a classification of the vectors of descriptors to build a vector of labels for each image. Finally, we propose three variants of Bayesian Networks such as Naïve Bayesia...

Naïve Bayes classifier has proved to be very effective on many real data applications. Its performances usually benefit from an accurate estimation of univariate conditional probabilities and from variable selection. However, although variable selection is a desirable feature, it is prone to overfitting. In this paper, we introduce a new regularization technique to select the most probable subs...

user-generated medical messages on internet contain extensive information related to adverse drug reactions (adrs) and are known as valuable resources for post-marketing drug surveillance. the aim of this study was to find an effective method to identify messages related to adrs automatically from online user reviews.we conducted experiments on online user reviews using different feature set an...

Document classification is a growing interest in the research of text mining. Correctly identifying the documents into particular category is still presenting challenge because of large and vast amount of features in the dataset. In regards to the existing classifying approaches, Naïve Bayes is potentially good at serving as a document classification model due to its simplicity. The aim of this...

The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, fact that violates the Bayes’ assumption of conditional independence, may deteriorate method’s performance. Moreover, datasets often characterized by large number features, which complicate interpretation results as well slow down execution. In...

A novel relational learning approach that tightly integrates the naı̈ve Bayes learning scheme with the inductive logic programming rule-learner FOIL is presented. In contrast to previous combinations that have employed naı̈ve Bayes only for post-processing the rule sets, the presented approach employs the naı̈ve Bayes criterion to guide its search directly. The proposed technique is implemented in...