Minimal Cost Complexity Pruning of Meta-Classifiers

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Student Abstracts

This extended abstract describes a pruning algorithm that is independent of the combining scheme and is used for discarding redundant classifiers without degrading the overall predictive performance of the pruned meta- classififier. To determine the most effective base classifiers, the algorithmtakes advantage of the minimal cost-complexity pruning method of the CART learning algorithm which guarantees to find the best (with respect to misclassification cost) pruned tree of a specific size (number of terminal nodes) of an initial unpruned decision tree.