Experiments on data classification using relative Entropy

Abstract

Data classification is one of the basic tasks in data mining. In this paper, we propose a new classifier based on relative entropy, where data to particular class assignment is made by the majority good guess criteria. The presented approach is intended to be used when relations between datasets and assignment classes are rather complex, nonlinear, or with logical inconsistencies; because such datasets can be too complex to be classified by ordinary methods of decision trees or by the tools of logical analysis. The relative entropy evaluation of associative rules can be simple to interpret and offers better comprehensibility in comparison to decision trees and artificial neural networks.