Journal article
Bagging with adaptive costs
IEEE transactions on knowledge and data engineering, Vol.20(5), pp.577-588
05/01/2008
DOI: 10.1109/TKDE.2007.190724
Abstract
Ensemble methods have proven 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. 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 with linear and nonlinear base classifiers'. In view of the characteristics of bacing, a hybrid ensemble learning strategy, which combines bagging and different versions of bacing, is proposed and studied empirically.
Details
- Title: Subtitle
- Bagging with adaptive costs
- Creators
- Yi Zhang - MicrosoftW. Nick Street - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on knowledge and data engineering, Vol.20(5), pp.577-588
- Publisher
- IEEE
- DOI
- 10.1109/TKDE.2007.190724
- ISSN
- 1041-4347
- eISSN
- 1558-2191
- Number of pages
- 12
- Language
- English
- Date published
- 05/01/2008
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380454502771
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