A nonlinear aggregation type classifier
This is an incremental contribution to functional data classification, potentially useful for researchers in statistics and machine learning.
The authors tackled the problem of classifying functional data by introducing a nonlinear aggregation classifier that combines multiple base classifiers. The result shows that this aggregation rule achieves consistency if the base classifiers are consistent and asymptotically performs as well as the best individual classifier.
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.