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...

This work implements an enhanced Bayesian classifier with better performance as compared to the ordinary naïve Bayes classifier when used with domains and datasets of varying characteristics. Text classification is an active and on-going research field of Artificial Intelligence (AI). Text classification is defined as the task of learning methods for categorising collections of electronic text ...

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...

Sentiment analysis is one among the distinguished fields of knowledge and pattern mining that deals with identification sentiment within text. The main challenges in are word ambiguity multi polarity. problem to define polarity because for words context dependent. tweets initially preprocessed. preprocessing includes removal stop words, lower case conversion. then passed feature extraction tech...

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...

today a significant part of available data is saved in text database or text documents. the most important thing is to organize these documents. one way to organize text documents is to classify them. to classify texts is to assign text documents to their actual categories. this has two main steps, i.e. feature- and learning algorithm selection. there have been several methods suggested to clas...