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A nonlinear aggregation type classifier

Author

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  • Cholaquidis, Alejandro
  • Fraiman, Ricardo
  • Kalemkerian, Juan
  • Llop, Pamela

Abstract

We introduce a nonlinear aggregation type classifier for functional data defined on a separable and complete metric space. The new rule is built up from a collection of M arbitrary training classifiers. If the classifiers are consistent, then so is the aggregation rule. Moreover, asymptotically the aggregation rule behaves as well as the best of the M classifiers. The results of a small simulation are reported both, for high dimensional and functional data, and a real data example is analyzed.

Suggested Citation

  • Cholaquidis, Alejandro & Fraiman, Ricardo & Kalemkerian, Juan & Llop, Pamela, 2016. "A nonlinear aggregation type classifier," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 269-281.
    • Handle: RePEc:eee:jmvana:v:146:y:2016:i:c:p:269-281
    DOI: 10.1016/j.jmva.2015.09.022
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    References listed on IDEAS

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    1. Aurore Delaigle & Peter Hall, 2012. "Achieving near perfect classification for functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 267-286, March.
    2. Peter Hall & Richard J. Samworth, 2005. "Properties of bagged nearest neighbour classifiers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 363-379, June.
    3. Mojirsheibani, Majid, 2002. "An Almost Surely Optimal Combined Classification Rule," Journal of Multivariate Analysis, Elsevier, vol. 81(1), pages 28-46, April.
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