Abstract: This paper presents a comparison of four representative discretization methods from different classes to be used with so called PGN-classifier which deals with categorical data. We examine which of them supplies more convenient discretization for PGN Classification Method. The experiments are provided on the base of UCI repository data sets. The comparison tests were provided using an experimental classification machine learning system "PaGaNe", which realizes Pyramidal Growing Network (PGN) Classification Algorithm. It is found that in general, PGN-classifier trained on data preprocessed by Chi-merge achieve lower classification error than those trained on data preprocessed by the other discretization methods. The comparison of PGN-classifier, trained with Chi-merge-discretizator with other classifiers (realized in WEKA system) shows good results in favor of PGNclassifier.

Keywords: Data Mining, Machine Learning, Discretization, Data Analysis, Pyramidal Growing Networks

Link:

COMPARISON OF DISCRETIZATION METHODS FOR PREPROCESSING DATA FOR PYRAMIDAL GROWING NETWORK CLASSIFICATION METHOD

Ilia Mitov, Krassimira Ivanova, Krassimir Markov, Vitalii Velychko, Peter Stanchev, Koen Vanhoof

http://www.foibg.com/ibs_isc/ibs-14/ibs-14-p04.pdf