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An asymptotically optimal kernel combined classifier

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  • Mojirsheibani, Majid
  • Kong, Jiajie

Abstract

A kernel ensemble classifier is developed for accurate classification based on several initial classifiers. A data-driven choice of the smoothing parameter of the kernel is considered and the resulting classifier is shown to be asymptotically optimal. Therefore, the proposed combined classifier asymptotically outperforms each individual classifier.

Suggested Citation

  • Mojirsheibani, Majid & Kong, Jiajie, 2016. "An asymptotically optimal kernel combined classifier," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 91-100.
    • Handle: RePEc:eee:stapro:v:119:y:2016:i:c:p:91-100
    DOI: 10.1016/j.spl.2016.07.017
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    References listed on IDEAS

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    1. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.
    2. Lin, Yi & Jeon, Yongho, 2006. "Random Forests and Adaptive Nearest Neighbors," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 578-590, June.
    3. van der Laan Mark J. & Polley Eric C & Hubbard Alan E., 2007. "Super Learner," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-23, September.
    4. Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
    5. Narayanaswamy Balakrishnan & Majid Mojirsheibani, 2015. "A simple method for combining estimates to improve the overall error rates in classification," Computational Statistics, Springer, vol. 30(4), pages 1033-1049, December.
    6. Yang, Yuhong, 2000. "Combining Different Procedures for Adaptive Regression," Journal of Multivariate Analysis, Elsevier, vol. 74(1), pages 135-161, July.
    7. Biau, Gérard & Fischer, Aurélie & Guedj, Benjamin & Malley, James D., 2016. "COBRA: A combined regression strategy," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 18-28.
    8. Adler, Werner & Brenning, Alexander & Potapov, Sergej & Schmid, Matthias & Lausen, Berthold, 2011. "Ensemble classification of paired data," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1933-1941, May.
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