ZDF: An adaptive discretization method to improve separability of classes

Distance-based classification methods use distances between un-labeled samples and labeled samples to assign a class label to it. In a problem space, the distance is computed based on attributes with different values. In continuous space, different values reflect different conditions and arise from problem´s nature, while differences in discretized datasets may be occurred as a result of discretization process. Thus, we propose a discretization method for optimal cut point place selection to minimize the inter-class distances. Experimental results show that the performance of our method in increasing the separability of samples in classes is significantly better than the results of entropy-based discretization.