Bayesian Network Classification: Application to Epilepsy Type Prediction Using PET Scan Data

Kamel Jebreen, Badih Ghattas

Abstract: Different types of Bayesian networks may be used for supervised classification. We combine such approaches together with feature selection and discretization and we show that such combination gives rise to powerful classifiers. A large choice of data sets from the UCI machine learning repository are used in our experiments and an application to Epilepsy type prediction based on PET scan data confirms the efficiency of our approach.