The main objective is to propose a text classification based on the features selection and preprocessing thereby reducing the dimensionality of the Feature vector and increase the classification accuracy. Text classification is the process of assigning a document to one or more target categories, based on its contents. In the proposed method, machine learning methods for text classification is ...

A Bayesian Learning Approach to Concept-Based Document Classification

A naive Bayesian classifier is a probabilistic classifier based on Bayesian decision theory with naive independence assumptions, which is often used for ranking or constructing a binary classifier. The theory of rough sets provides a ternary classification method by approximating a set into positive, negative and boundary regions based on an equivalence relation on the universe. In this paper, ...

Network traffic classification can be used to identify different applications and protocols that exist in a network. Actions such as monitoring, discovery, control and optimization can be performed by using classified network traffic. The overall goal of network traffic classification is improving the network performance. Once the packets are classified as belonging to a particular application,...

In this paper, we propose a Bayesian network-based trust model in peerto-peer networks. Since trust is multi-faceted, even in the same context, peers still need to develop differentiated trust in different aspects of other peers’ behaviors. The peer’s needs are different in different situations. Depending on the situation, a peer may need to consider its trust in a specific aspect of another pe...

Naive Bayesian Classifiers, which rely on independence hypotheses, together with a normality assumption to estimate densities for numerical data, are known for their simplicity and their effectiveness. However, estimating densities, even under the normality assumption, may be problematic in case of poor data. In such a situation, possibility distributions may provide a more faithful representat...

The problem of clustering, or unsupervised classification, has been solved by a myriad of techniques, all of which depend, either directly or implicitly, on the Bayesian principle of optimal classification. To be more specific, within a Bayesian paradigm, if one is to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be...

The amount of data present online is growing very rapidly, hence a need for organizing and categorizing data has become an obvious need. The Information Retrieval (IR) techniques act as an aid in assisting users in obtaining relevant information. IR in the Indian context is very relevant as there are several blogs, news publications in Indian languages present online. This work looks at the sui...

Multi-dimensional Bayesian network classifiers (MDCs) generalise the popular robustly performing one-dimensional classifiers (ODCs) to application domains that require an instance to be classified into a combination of classes. In previous work we compared the sensitivity of MDC and ODC output probabilities to small parameter inaccuracies. In this paper we extend our analyses and study the robu...

We propose a Bayesian approach to multiple testing in disease mapping. This study was motivated by a real example regarding the mortality rate for lung cancer, males, in the Tuscan region (Italy). The data are relative to the period 1995-1999 for 287 municipalities. We develop a tri-level hierarchical Bayesian model to estimate for each area the posterior classification probability that is the ...