Bagging with Adaptive Costs

9 years 5 months ago
Bagging with Adaptive Costs
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.
Yi Zhang, W. Nick Street
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where ICDM
Authors Yi Zhang, W. Nick Street
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