A nonlinear aggregation type classifier

Alejandro Cholaquidis, Ricardo Fraiman, Juan Kalemkerian and Pamela Llop

Journal of Multivariate Analysis, 2016, vol. 146, issue C, 269-281

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.

Keywords: Functional data; Supervised classification; Non-linear aggregation (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1016/j.jmva.2015.09.022

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