Performance Improvement for Bayesian Classification on Spatial Data with P-Trees

Accuracy is one of the major issues for a classifier. Currently there exist a range of classifiers with different degrees of accuracy directly related to computational complexity. In this paper we are presenting an approach to improve the classification accuracy of an existing PTree based Bayesian classification technique. The new approach has increased the granularity between two conditional probability calculations by using a bit-based approach rather than the existing band-based approach. This approach enables the complete elimination of the naïve assumption. The new approach maintains the same computational cost as the previous method. This approach outperforms the existing P-Tree based Bayesian classifier, a Bayesian belief network and a Euclidian distance based KNN classifier, in terms of accuracy for a particular set of spatial data collected for precision agriculture.