On look-ahead and pathology in decision tree learning
In decision tree learning, attribute selection is usually based on a greedy local splitting criterion. More extensive search is time consuming and usually does not benefit prediction accuracy as much as one would hope. It has even been claimed that look-ahead would be harmful in decision tree learning. The pathological effects of look-ahead have been studied analytically in the context of game trees. These results and their connection to decision tree learning are considered. A computationally efficient splitting algorithm for numerical domains is presented, which is based on ideas from search tree look-ahead. In empirical tests, the algorithm performs in a promising manner.
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Document Type: Research Article
Affiliations: Department of Computer Science University of Helsinki Finland
Publication date: January 1, 2005