A geometric approach to feature selection

Abstract

We propose a new method for selecting features, or deciding on splitting points in inductive learning. Its main innovation is to take the positions of examples into account instead of just considering the numbers of examples from different classes that fall at different sides of a splitting rule. The method gives rise to a family of feature selection techniques. We demonstrate the promise of the developed method with initial empirical experiments in connection of top-down induction of decision trees.