SLIT: Designing Complexity Penalty for Classification and Regression Trees Using the SRM Principle

Abstract

The statistical learning theory has formulated the Structural Risk Minimization (SRM) principle, based upon the functional form of risk bound on the generalization performance of a learning machine. This paper addresses the application of this formula, which is equivalent to a complexity penalty, to model selection tasks for decision trees, whereas the quantization of the machine capacity for decision trees is estimated using an empirical approach. Experimental results show that, for either classification or regression problems, this novel strategy of decision tree pruning performs better than alternative methods. We name classification and regression trees pruned by virtue of this methodology as Statistical Learning Intelligent Trees (SLIT).