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OFAI-TR-97-07 ( 55kB g-zipped PostScript file)

Noise-tolerant Windowing

Johannes Fürnkranz

Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, previous work has shown that it may often lead to a decrease in performance, in particular in noisy domains. Following up on previous work, where we have shown that the ability of separate-and-conquer rule learning algorithms to learn rules independently can be exploited for more efficient windowing procedures, we demonstrate in this paper how this property can be exploited to achieve noise-tolerance in windowing.

Citation: Fürnkranz J.: Noise-tolerant Windowing, in Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 852-857, Nagoya, Japan, 1997.