Efficient Determination of Dynamic Split Points in a Decision Tree

• Authors: David Maxwell Chickering;Christopher Meek;Robert Rounthwaite
• Venue: ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
• Year: 2001

We consider the problem of choosing split points forcontinuous predictor variables in a decision tree. Previousapproaches to this problem typically either (1) discretize the continuous predictor values prior to learning or (2) apply a dynamic method that considers all possible split points for each potential split. In this paper, we describe anumber of alternative approaches that generate a smallnumber of candidate split points dynamically with littleoverhead. We argue that these approaches are preferable to pre-discretization, and provide experimental evidence that they yield probabilistic decision trees with the same prediction accuracy as the traditional dynamic approach.Furthermore, because the time to grow a decision tree isproportional to the number of split points evaluated, our approach is significantly faster than the traditional dynamic approach.