Aggregating classifiers with mathematical programming
Bagging and boosting are popular and often successful ways to improve the performance of a classifier by means of aggregation. Classifiers can also be aggregated by means of an efficient and flexible mathematical programming model. This data-based approach guarantees that the aggregated classifier will be at least as good as the best predictor on the design data set for a user-defined criterion function. The mathematical programming approach is evaluated on real-world data sets from different contexts such as medical diagnosis, image segmentation and handwritten digit recognition. The real-world examples show that the approach can outperform both bagging and boosting.
mathematical programming bagging boosting supervised classification computational efficiency