This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming , most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning , is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of this algorithm cannot be recommended for domains with a very specific concept description. OEFAI-TR-94-09 1 Introduction Being able to deal with noisy data is a must for algorithms that are meant to learn concepts in real-world domains. Significant effort has gone into investigating the effect of noisy data on decision tree learning algorithms (see e.g. [Quinlan, 1993, Breiman et al., 1984]). Not surprisingly, noise handling methods have also entered the emerging field of Inductive Logic Programming (ILP) [Muggleton, 1992]. Linus [Lavr...

Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre- and post-pruning techniques for separate-and-conquer rule learning algorithms and discuss some fundamental problems. The primary goal of this paper is to show how to solve these problems with two new algorithms that combine and integrate pre- and post-pruning.