Efficient Incremental Induction of Decision Trees

  • Kalles D
  • Morris T
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This paper proposes a method to improve ID5R, an incremental TDIDT algorithm. The new method evaluates the quality of attributes selected at the nodes of a decision tree and estimates a minimum number of steps for which these attributes are guaranteed such a selection. This results in reducing overheads during incremental learning. The method is supported by theoretical analysis and experimental results.

Author-supplied keywords

  • Incremental algorithm
  • decision tree induction

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  • Dimitrios Kalles

  • Tim Morris

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