Bagging with Adaptive Costs

Authors:
Yi Zhang;W. Nick Street
Affiliations:
University of Iowa;University of Iowa
Venue:
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Year:
2005

Citing 6
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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 — building the classifier the ensemble needs — without fixating on potentially noisy points. Computational experiments show that this algorithm performs consistently better than bagging and arcing.