Top-down induction of logical decision trees
Hendrik Blockeel, Luc De Raedt · Lirias (KU Leuven) · 1997
A first order framework for top-down induction of logical decision trees is introduced. Logical decision trees are more expressive than the flat logic programs typically induced by empirical inductive logic programming systems because logical decision trees introduce invented predicates and mix existential and universal quantification of variables. An implementation of the framework, the Tilde system, is presented and empirically evaluated. 1 Introduction Top-down induction of decision trees (TDIDT) [Qui86] is the best known and most succesful machine learning technique. It has been used to solve numerous practical problems. It employs a divide-and-conquer strategy, and in this it differs from its rulebased competitors (e.g. AQ [MMHL86]), which are based on covering strategies (cf. [Bos95]). Within attribute-value learning (or propositional concept-learning) TDIDT is more popular than the covering approach. Yet, within first order approaches to concept-learning, only a few learning sy...