Discretizing environmental data for learning Bayesian-network classifiers

Abstract

For predicting the presence of different bird species in Andalusia from land-use data, we compare the performances of Bayesian-network classifiers and logistic-regression models. In our study, both well balanced and less balanced data sets are used, and models are learned from both the original continuous data and from the data after discretization. For the latter purpose, four different discretization methods, called Equal Frequency, Equal Width, Chi-Merge and MDLP, are compared. The experimental results from our species data sets suggest that the simple Naive Bayesian classifiers are preferable to logistic-regression models and that the relatively unknown Chi-Merge method is the preferred method for discretizing these environmental data.