A nonlinear aggregation type classifier

JOURNAL OF MULTIVARIATE ANALYSIS

卷:146

Article

Cholaquidis, Alejandro¹  Fraiman, Ricardo¹  Kalemkerian, Juan¹  Llop, Pamela^2,3 

[1] Univ Republ, Fac Ciencias, Ctr Matemat, Asuncion, Paraguay

[2] UNL CONICET, Inst Matemat Aplicada Litoral, Buenos Aires, DF, Argentina

[3] UNL, Fac Ingn Quim, Dept Matemat, Buenos Aires, DF, Argentina

关键词: Functional data;    Supervised classification;    Non-linear aggregation;   

DOI  :  10.1016/j.jmva.2015.09.022

来源: Elsevier

【 摘 要 】

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. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free