traffic prediction systems can play an essential role in intelligent transportation systems (its). prediction and patterns comprehensibility of traffic characteristic parameters such as average speed, flow, and travel time could be beneficiary both in advanced traveler information systems (atis) and in its traffic control systems. however, due to their complex nonlinear patterns, these systems ...

Different probabilistic models for classification and prediction problems are anlyzed in this article studying their behaviour and capability in data classification. To show the capability of Bayesian Networks to deal with classification problems four types of Bayesian Networks are introduced, a General Bayesian Network, the Naive Bayes, a Bayesian Network Augmented Naive Bayes and the Tree Aug...

Introduction: This research was meant to provide a model for COPD diagnosis and to classify the cases into phenotypes; General COPD, Chronic bronchitis, Emphysema, and the Asthmatic COPD using a Bayesian Network (BN). Methods: The model was constructed through developing the Bayesian Network structure and instantiating the parameters for each of the variables. In order to validate the achiev...

This paper describes a Bayesian technique for unsupervised classification of data and its computer implementation, Autoclass. Given real valued or discrete data, AutoClass automatically determines the most probable number of classes present in the data, the most probable descriptions of those classes, and each object's probability of membership in each class. The program performs as well as or ...

In this paper we study the application of bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of bayesian networks as: Naive Bayes, Tree Augmented Naive Bayes, Forest Augmented Naive Bayes and General Bayesian Networks, are applied in the classification of hyperspectral data. In addition, several bayesian multi-net models are applied in th...