[ ILPnet2 | Library | Newsletter | CSCW | Education | End-User Club | Events | Nodes | Systems | Applications | Members only ] In this work we upgrade induction of decision trees and some related sophisticated techniques from AVL to ILP. To this aim we first define a relatively general form of induction that we call predictive clustering. We demonstrate that a number of important tasks (classification, regression) are special cases of predictive clustering. Next, we discuss an algorithm for induction of decision trees that generalizes over many existing algorithms for induction of classification or regression trees.
In a second part we define decision trees in the context of first order logic, which is the representation formalism used in ILP, and study their properties. Once these first order logical decision trees are defined and understood, it becomes possible to apply the proposed technique for predictive clustering within ILP. We present an implementation of the algorithm and evaluate it empirically. It turns out that the resulting program is competitive with state-of-the-art ILP systems; however it is more generally applicable and often faster.