Minimal Cost Complexity Pruning of Meta-Classifiers

Andreas L. Prodromidis and Salvatore J. Stolfo, Columbia University

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.


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