Tree Augmented Naive Classifier Research Articles

Using Bayesian networks for environmental health risk assessment.

The study investigated the potential relationships between air pollution, socio-economy, and proven pathologies (e.g., respiratory, cardiovascular) within an industrial area in Southern France (Etang de Berre), gathering steel industries, oil refineries, shipping, road traffic and experiencing a Mediterranean climate. A total of 178 variables were simultaneously integrated within a Bayesian model at intra-urban scale. Various unsupervised and supervised algorithms (maximum spanning tree, tree-augmented naive classifier) as well as sensitivity analyses were used to better understand the links between all variables, and highlighted correlations between population exposure to air pollutants and some pathologies. Adverse health effects (bronchus and lung cancers for 15-65 years old people) were observed for hydrofluoric acid at low background concentration (<0.003μgm-3) while exposure to particulate cadmium (0.210-0.250μgm-3) disrupts insulin metabolism for people over 65 years-old leading to diabetes. Bronchus and lung cancers for people over 65 years-old occurred at low background SO2 concentration (6μgm-3) below European limit values. When benzo[k]fluoranthene exceeded 0.672μgm-3, we observed a high number of hospital admissions for respiratory diseases for 15-65 years-old people. The study also revealed the important influence of socio-economy (e.g., single-parent family, people with no qualification at 15 years-old) on pathologies (e.g., cardiovascular diseases). Finally, a diffuse polychlorinated biphenyl (PCB) pollution was observed in the study area and can potentially cause lung cancers.

Averaged Extended Tree Augmented Naive Classifier

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required.