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Abstract: Classifier committee learning approaches have demonstrated great success in increasing the prediction accuracy of classifier learning, which is a key technique for datamining. These approaches generate several classifiers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the final classification. It has been shown that Boosting and Bagging, as two representative methods of this type, can significantly decrease the error rate of ... (Update)
Context of citations to this paper: More
...of SascB and Boosting. It generates multiple subcommittees by incorporating Bagging into SascB using the multiboosting technique [15]. We expect that splitting one committee into multiple subcommittees, with each subcommittee being created from a bootstrap sample of the...
.... modification to employ bagging rather than wagging of sub committees, as was performed in a variant of MultiBoost described by Zheng and Webb (1998). To test this hypothesis, MultiBoost was modified appropriately. Table 22 summarizes the relative error of the two MultiBoost...
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BibTeX entry: (Update)
Zheng, Z. and Webb, G.I.: Multiple Boosting: A combination of Boosting and Bagging. Proceedings of the 4th International Conference on Parallel and Distributed Processing Techniques and Applications. CSREA Press (1998b) 1133-1140. http://citeseer.ist.psu.edu/article/zheng98multiple.html More
@misc{ zheng-multiple,
author = "Z. Zheng and G. Webb",
title = "Multiple Boosting: A combination of Boosting and Bagging",
text = "Zheng, Z. and Webb, G.I.: Multiple Boosting: A combination of Boosting
and Bagging. Proceedings of the 4th International Conference on Parallel
and Distributed Processing Techniques and Applications. CSREA Press (1998b)
1133-1140.",
url = "citeseer.ist.psu.edu/article/zheng98multiple.html" }
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