Relational instance-based learning
Werner Emde, Dietrich Wettschereck · 1996
A relational instance-based learning algorithm, called Ribl, is motivated and developed in this paper. We argue that instancebased methods o#er solutions to the often unsatisfactory behavior of current inductive logic programming #ILP# approaches in domains with continuous attribute values and in domains with noisy attributes and#or examples. Three research issues that emerge when a propositional instance-based learner is adapted to a #rst-order representation are identi#ed: #1# construction of cases from the knowledge base, #2# computation of similaritybetween arbitrarily complex cases, and #3# estimation of the relevance of predicates and attributes. Solutions to these issues are developed. Empirical results indicate that Ribl is able to achieve high classi#cation accuracy in a variety of domains. to appear in: Proc. 13th International Conference on Machine Learning, L. Saitta #ed.#, Morgan Kaufmann, 1996 1 Introduction The #eld of Inductive Logic Programming ...