Weighted Mutual Information for Feature Selection

Erik Schaffernicht, Horst-Michael Gross

Abstract: In this paper, we apply weighted Mutual Information for effective feature selection. The presented hybrid filter wrapper approach resembles the well known AdaBoost algorithm by focusing on those samples that are not classified or approximated correctly using the selected features. Redundancies and bias of the employed learning machine are handled implicitly by our approach.