The design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability density functions and therefore requires a design sample size that increases exponentially with the number of dimensions. A method was developed that combines classification decisions from marginal density functions using an additional classifier. Unlike voting...

Classifying human cognitive states using fMRI data is an interesting and challenging problem that would guide us to better understanding about human brain. In this paper we introduce TAN classifier to distinguish whether subjects are examining a sentence or a picture. Experiments show that using this TAN classifiers provides significantly better results than using Naive Bayes classifiers.

Prediction analysis is a definite need of any business sector for retaining and attracting the most valuable customers .It is considered as a major challenge facing companies in this information age. Data mining enables companies, in the context of defined business objectives, discover new knowledge, to explore, visualise and understand their data, and to identify patterns, relationships and de...

The design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability density functions and therefore requires a design sample size that increases exponentially with the number of dimensions. A method was developed that combines classifications from marginal density functions using an additional classifier. Unlike voting methods,...

In this paper, we examine previous work on the naive Bayesian classiier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space of features. We hypothesize that this approach will improve asymptotic accuracy in domains that in...

Feature selection is widely used as the first stage of classification task to reduce the dimension of problem, decrease noise, improve speed and relieve memory constraints by the elimination of irrelevant or redundant features. One approach in the feature selection area is employing population-based optimization algorithms such as particle swarm optimization (PSO)-based method and ant colony op...