ReMauve: A relational model tree learner

Abstract

Model trees are a special case of regression trees in which linear regression models are constructed in the leaves. Little attention has been paid to model trees in relational learning, mainly because the task of learning linear regression equations in this context involves dealing with non-determinacy of predictive attributes. Whereas existing approaches handle this non-determinacy issue either by selecting a single value or by aggregating over all values, in this paper we present a model tree learning system that combines both. © Springer-Verlag Berlin Heidelberg 2007.