CN2-MCI: A Two-Step Method for Constructive Induction

Methods for constructive induction perform an automatic transformation of description spaces if representational shortcomings deteriorate the quality of learning. In the context of concept learning and propositional representation languages, feature construction algorithms have been developed in order to improve the accuracy and to decrease the complexity of hypotheses. Particularly, so-called hypothesis-driven constructive induction (HCI) algorithms construct new attributes based upon the analysis of induced hypotheses. Well-known HCI-systems analyze decision trees, or employ a coarse-grained analysis of decision rules. This paper introduces a new constructive operator o and documents its applicability in the usual HCI-framework. o uses a cluster algorithm to map selected features into a new binary feature. A new method for constructive induction, CN2-MCI, is described that applies o as its only constructive operator to achieve a ne-grained analysis of decision rules. The output of CN2-MCI is an inductive hypothesis expressed in terms of the transformed representation, given training examples as input. CN2-MCI is theoretically and empirically compared with existing methods for constructive induction.