A Comparison of Pruning Methods for Relational Concept Learning

Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are more accurate, but much slower, because they have to generate an overly specific concept description first. We have experimented with a variety of pruning methods, including two new methods that try to combine and integrate preand postpruning in order to achieve both accuracy and efficiency. This is verified with test series in a chess position classification task.