Machine Learning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Discriminant Analysis and Factorial Multiple Splits in Recursive Partitioning for Data Mining
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Supervised Classifier Combination through Generalized Additive Multi-model
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Conditional classification trees using instrumental variables
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
A statistical approach to growing a reliable honest tree
Computational Statistics & Data Analysis
| Hi-index | 0.00 |
This paper provides a faster method to find the best split at each node when using the CART methodology. The predictability index τ is proposed as a splitting rule for growing the same classification tree as CART does when using the Gini index of heterogeneity as an impurity measure. A theorem is introduced to show a new property of the index τ: the τ for a given predictor has a value not lower than the τ for any split generated by the predictor. This property is used to make a substantial saving in the time required to generate a classification tree. Three simulation studies are presented in order to show the computational gain in terms of both the number of splits analysed at each node and the CPU time. The proposed splitting algorithm can prove computational efficiency in real data sets as shown in an example.