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On Lookahead Heuristics in Decision Tree Learning
2003In 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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On Handling Tree-Structured Attributes in Decision Tree Learning
1995Abstract This paper studies the problem of learning decision trees when the attributes of the domain are tree-structured. We first describe two pre-processing approaches, the Quinlan- encoding and the bit-per-category methods, that re-encode the training examples in terms of new nominal attributes.
Hussein Almuallim +2 more
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