Abstract
This paper outlines some problems that may occur with Reduced Error Pruning in rule learning algorithms. In particular we show that pruning complete theories is incompatible with the separate-and-conquer learning strategy that is commonly used in propositional and relational rule learning systems. As a solution we propose to integrate pruning into learning and examine two algorithms, one that prunes at the clause level and one that prunes at the literal level. Experiments show that these methods are not only much more efficient, but also able to achieve small gains in accuracy by solving the outlined problem.
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References
Clifford A. Brunk and Michael J. Pazzani. An investigation of noise-tolerant relational concept learning algorithms. In Proceedings of the 8th International Workshop on Machine Learning, pages 389–393, Evanston, Illinois, 1991.
William W. Cohen. Efficient pruning methods for separate-and-conquer rule learning systems. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 988–994, Chambery, France, 1993.
Johannes Fürnkranz and Gerhard Widmer. Incremental Reduced Error Pruning. In Proceedings of the 11th International Conference on Machine Learning, pages 70–77, New Brunswick, NJ, 1994.
Johannes Fürnkranz. A tight integration of pruning and learning. Technical Report OEFAI-TR-95-03, Austrian Research Institute for Artificial Intelligence, 1995.
John Ross Quinlan. Learning logical definitions from relations. Machine Learning, 5:239–266, 1990.
Sholom M. Weiss and Nitin Indurkhya. Small sample decision tree pruning. In Proceedings of the 11th Conference on Machine Learning, pages 335–342, Rutgers University, New Brunswick, NJ, 1994.
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© 1995 Springer-Verlag Berlin Heidelberg
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Fürnkranz, J. (1995). A tight integration of pruning and learning (Extended abstract). In: Lavrac, N., Wrobel, S. (eds) Machine Learning: ECML-95. ECML 1995. Lecture Notes in Computer Science, vol 912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59286-5_70
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DOI: https://doi.org/10.1007/3-540-59286-5_70
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