Feature Selection Based on Information Theory Filters

Abstract

Feature selection is an essential component in all data mining applications. Ranking of futures was made by several inexpensive methods based on information theory. Accuracy of neural, similarity based and decision tree classifiers calculated with reduced number of features. Comparison with computationally more expensive feature elimination methods was made.