Efficient Incremental Induction of Decision Trees
Tsaopoulos Dimitrios · 2004
This paper proposes a method to improve 1D5R, 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.