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.
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