Bagging with Adaptive Costs Yi Zhang and Nick Street   View Paper (PDF)   Return to Session 2: Clustering Schemes I Abstract Ensemble methods have proved to be highly effective in improving the performance of base learners under most circumstances. In this paper, we propose a new algorithm that combines the merits of some existing techniques, namely bagging, arcing and stacking. The basic structure of the algorithm resembles bagging, using a linear support vector machine (SVM). However, the misclassification cost of each training point is repeatedly adjusted according to its observed out-of-bag vote margin. In this way, the method gains the advantage of arcing ¡X building the classifier the ensemble needs ¡X without fixating on potentially noisy points. Computational experiments show that this algorithm performs consistently better than bagging and arcing. ©2006 Association for Computing Machinery