Abstract
Boosting algorithms combine moderately accurate classifiers in order to produce highly accurate ones. The most important boosting algorithms are Adaboost and Arc-x(j). While belonging to the same algorithms family, they differ in the way of combining classifiers. Adaboost uses weighted majority vote while Arc-x(j) combines them through simple majority vote. Breiman (1998) obtains the best results for Arc-x(j) with j = 4 but higher values were not tested. Two other values for j, j = 8 and j = 12 are tested and compared to the previous one and to Adaboost. Based on several real binary databases, empirical comparison shows that Arc-x4 outperforms all other algorithms.
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© 2005 Springer-Verlag Berlin · Heidelberg
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Khanchel, R., Limam, M. (2005). Empirical Comparison of Boosting Algorithms. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_16
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DOI: https://doi.org/10.1007/3-540-28084-7_16
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