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On Lookahead Heuristics in Decision Tree Learning

2003
In decision tree learning attribute selection is usually based on greedy local splitting criterion. More extensive search quickly leads to intolerable time consumption. Moreover, it has been observed that lookahead cannot benefit prediction accuracy as much as one would hope.
Tapio Elomaa, Tuomo Malinen
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Learning decision tree classifiers

ACM Computing Surveys, 1996
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