Abstract
In this paper, we investigate how the diversity of nominal classifier ensembles affects the AdaBoost performance [13]. Using 5 real data sets from the UCI Machine Learning Repository and 3 different diversity measures, we show that \(\mathcal{Q}\) Statistic measure is mostly correlated with AdaBoost performance for 2-class problems. The experimental results suggest that the performance of AdaBoost depend on the nominal classifier diversity that can be used as a stopping criteria in ensemble learning.
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Meddouri, N., Khoufi, H., Maddouri, M.S. (2012). Diversity Analysis on Boosting Nominal Concepts. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_26
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DOI: https://doi.org/10.1007/978-3-642-30217-6_26
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