Landscapes of Naive Bayes classifiers

The performance of the Naive Bayes classifier (NB) is of interest to many researchers. The desire to improve upon the apparent good performance of NB while maintaining its efficiency and simplicity is demonstrated by the variety of adaptations to NB in the literature. This study takes a look at 37 such adaptations. The idea is to give a qualitative overview of the adaptations rather than a quantitative analysis of their performance. Landscapes are produced using Sammon mapping, Principal Component Analysis (PCA) and Self-Organising feature Maps (SOM). Based on these, the methods are split into five main groups-tree structures, feature selection, space transformation, Bayesian networks and joint features. The landscapes can also be used for placing any new variant of NB to obtain its nearest neighbours as an aid for comparison studies.