A Further Comparison of Splitting Rules for Decision-Tree Induction |
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Authors: | Wray Buntine Tim Niblett |
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Affiliation: | (1) The Turing Institute, George House, 36 North Hanover St, Glasgow, G1 2AD, U.K;(2) Research Institute for Advanced Computer Science and Artificial Intelligence Research Branch, NASA Ames Research Center, Mail Stop 269-2, Moffett Field, CA 94035, USA |
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Abstract: | One approach to learning classification rules from examples is to build decision trees. A review and comparison paper by Mingers (Mingers, 1989) looked at the first stage of tree building, which uses a splitting rule to grow trees with a greedy recursive partitioning algorithm. That paper considered a number of different measures and experimentally examined their behavior on four domains. The main conclusion was that a random splitting rule does not significantly decrease classificational accuracy. This note suggests an alternative experimental method and presents additional results on further domains. Our results indicate that random splitting leads to increased error. These results are at variance with those presented by Mingers. |
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Keywords: | Decision trees induction noisy data comparative studies |
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