Cost Complexity Pruning of Ensemble Classifiers

In this paper we study methods that combine multiple classification models learned over separate data sets in a distributed database setting. Numerous studies posit that such approaches provide the means to efficiently scale learning to large datasets, while also boosting the accuracy of individual classifiers. These gains, however, come at the expense of an increased demand for run-time system resources. The final ensemble meta-classifier may consist of a large collection of base classifiers that require increased memory resources while also slowing down classification throughput. Here, we present a technique for measuring the tradeoff between predictive performance and available run time system resources and we describe an algorithm for pruning (i.e. discarding a subset of the available base classifiers) the ensemble metaclassifier as a means to reduce its size while preserving its accuracy. The algorithm is independent of the method used initially when computing the meta-classifier. It is based on decision tree pruning methods and relies on the mapping of an arbitrary ensemble meta-classifier to a decision tree model. Through an extensive empirical study on metaclassifiers computed over two real data sets, we illustrate our pruning algorithm to be a robust approach to discarding classification models without degrading the overall predictive performance of an ensemble computed over those that remain after pruning.