Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...

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...

Bayes-N is an algorithm for Bayesian network learning from data based on local measures of information gain, applied to problems in which there is a given dependent or class variable and a set of independent or explanatory variables from which we want to predict the class variable on new cases. Given this setting, Bayes-N induces an ancestral ordering of all the variables generating a directed ...

This paper introduces a new Bayesian network structure, named a Partial Bayesian Network (PBN), and describes an algorithm for constructing it. The PBN is designed to be used for classification tasks, and accordingly the algorithm constructs an approximate Markov blanket around a classification node. Initial experiments have compared the performance of the PBN algorithm with Naïve Bayes, Tree-A...

Bayesian networks are models for uncertain reasoning which are achieving a growing importance also for the data mining task of classification. Credal networks extend Bayesian nets to sets of distributions, or credal sets. This paper extends a state-of-the-art Bayesian net for classification, called tree-augmented naive Bayes classifier, to credal sets originated from probability intervals. This...

Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using ‘unsupervised’ methods such as maximization of the joint likelihood. The reason is often that it is unclear how to find the parameters maximizing the conditional (supervised) likelihood. We show how the discriminative learning problem can be solved ef...

Discretization of continuous attributes is an important task in rough sets and many discretization algorithms have been proposed. However, most of the current discretization algorithms are univariate, which may reduce the classification ability of a given decision table. To solve this problem, we propose a supervised and multivariate discretization algorithm — SMDNS in rough sets, which is deri...

in today world of internet, it is important to feedback the users based on what they demand. moreover, one of the important tasks in data mining is classification. today, there are several classification techniques in order to solve the classification problems like genetic algorithm, decision tree, bayesian and others. in this article, it is attempted to classify researchers to “expert” and “no...

Distributed Data Mining (DDM) has been proposed as a means to deal with the analysis of distributed data, where DDM discovers patterns and implements prediction based on multiple data sources. However, faces several problems in terms autonomy, privacy, performance implementation. requires homogeneity regarding environment, control, administration classification algorithm(s), such that requireme...