Generation of globally relevant continuous features for classification

Abstract

All learning algorithms perform very well when provided with a small number of highly relevant features. This paper proposes a constructive induction method to automatically construct such features. The method, named GLOREF (GLObally RElevant Features), exploits low-level interactions between the attributes in order to generate globally relevant features. The usefulness of the approach is demonstrated empirically through a large scale experiment involving 13 classifiers and 24 datasets. Results demonstrate the ability of the method in generating highly informative features and a strong positive effect on the accuracy of the classifiers. © 2008 Springer-Verlag Berlin Heidelberg.