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Efficient Pruning Methods for Relational Learning

Johannes Fürnkranz

This thesis is concerned with efficient methods for achieving noise-tolerance in Machine Learning algorithms that are capable of using relational background knowledge. While classical algorithms are restricted to learn propositional concepts in the form of decision trees or decision lists, relational learning algorithms are able to include into the learning process not only knowledge about data attributes and values, but also about relations between the attributes. As these algorithms use a more powerful representation language --- they learn PROLOG programs for classification --- they are part of the recent field of Inductive Logic Programming, a new research area at the intersection of Machine Learning and Logic Programming. In this work we first review several known methods for achieving noise-tolerance and put them into a unified framework and then introduce three new and improved algorithms. The two basic approaches to pruning are either to try to recognize noise in the data during the learning process (pre-pruning) or to first learn a theory from the data as they are and subsequently try to detect and correct the resulting mistakes in this theory (post-pruning). Both approaches having their advantages, the major part of this thesis is devoted to trying to combine and integrate them into new powerful algorithms. A series of experiments with artificial and natural data sets demonstrates the usefulness of these approaches.

Citation: Fürnkranz J.: Efficient Pruning Methods for Relational Learning, Ph.D. Thesis, Technical University of Vienna, Austria, November 1994.