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.

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Yi Zhang, W. Nick Street
• Keywords: Algorithm, Arcing, Data Mining, ICDM 2005, Observed Out-of-bag Vote