Information Theory and Classification Error in Probabilistic Classifiers

This work shows, using bivariate continuous artificial domains, the relation that seems to exist between some measures based on the information theory and the expected classification error. The relations that seem to be found in this work could be applied to the improvement of the classifiers which assign a posteriori probabilities to each class value. They also could be used in other tasks related to the supervised classification such as feature subset selection or discretization.