Conference proceeding
Bagging with adaptive costs
Fifth IEEE International Conference on Data Mining (ICDM'05), pp.4 pp-828
2005
DOI: 10.1109/ICDM.2005.32
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.
Details
- Title: Subtitle
- Bagging with adaptive costs
- Creators
- Yi Zhang - Dept. of Manage. Sci., Iowa Univ., IA, USAW.N. Street - Dept. of Manage. Sci., Iowa Univ., IA, USA
- Resource Type
- Conference proceeding
- Publication Details
- Fifth IEEE International Conference on Data Mining (ICDM'05), pp.4 pp-828
- Publisher
- IEEE
- DOI
- 10.1109/ICDM.2005.32
- ISSN
- 1550-4786
- eISSN
- 2374-8486
- Language
- English
- Date published
- 2005
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380381602771
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