Pruning Methods for Rule Learning Algorithms
Johannes Fürnkranz · 1994
In this paper we will shortly review several pruning methods for relational learning algorithms and show how they are related to each other. We then report some experiments in several natural domains and try to analyse the performance of the algorithms in these domains in terms of runtime and accuracy. While some algorithms are clearly faster than others, no safe recommendation for achieving high accuracy can be given. 1 Introduction Lately several pruning methods for noise handling in relational rule learning algorithms have been proposed. The classic approaches to pruning are based on pre-pruning (Foil [Quinlan, 1990], mFoil [Dzeroski and Bratko, 1992], or Fossil [Furnkranz, 1994b]) and post-pruning (Reduced Error Pruning (REP) [Brunk and Pazzani, 1991] and Grow [Cohen, 1993]). More recently approaches have been proposed that combine (MDL-Grow [Cohen, 1993] and Top Down Pruning (TDP) [Furnkranz, 1994c]) and integrate (Incremental Reduced Error Pruning (I-REP) [Furnkranz and Widmer,...