This paper describes a new framework for using natural selection to evolve Bayesian Networks for use in forecasting time series data. It extends current research by introducing a tree based representation of a candidate Bayesian Network that addresses the problem of model identification and training through the use of natural selection. The framework constructs a modified Naïve Bayesian classif...

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...

Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method using both data mining and statistical tasks, utilizing feature selection, resampling and cost-...

A molecular similarity searching technique based on Atom Environments, information-gain based feature selection and the Naïve Bayesian Classifier has been applied to a series of diverse datasets and its performance compared to alternative searching methods. In this application, using a recently published dataset of more than 100,000 molecules from the MDL Drug Data Report (MDDR) database, the A...

We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on probabilistic graphical models. These include two mechanisms for adding noise to the Bayesian updates, either directly to the posterior parameters, or to their Fourier tr...

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 ...

It is a well-known fact that the Bayesian Networks’ (BNs) use as classifiers in different fields of application has recently witnessed a noticeable growth. Yet, the Naïve Bayes’ application, and even the augmented Naïve Bayes’, to classifier-structure learning, has been vulnerable to certain limits, which explains the practitioners’ resort to other more sophisticated types of algorithms. Conseq...