EFFICIENT INCREMENTAL INDUCTION OF DECISION TREES

This paper proposes a method to improve ID5R, an incremental TDIDT alg orithm. 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 resul ts in reducing overheads during incremental learning. The method is su pported by theoretical analysis and experimental results.