A new k-nearest neighbors classifier for functional data

For supervised classification of functional data, several classifiers have been proposed in the literature, including the well-known classic k-nearest neighbors (kNN) classifier. The classic kNN classifier selects k nearest neighbors around a new observation and determines its class-membership according to a majority vote. A difficulty arises when there are two classes having the same largest number of votes. To overcome this difficulty, we propose a new kNN classifier which selects k nearest neighbors around a new observation from each class. The class-membership of the new observation is determined by the minimum average distance or semi-distance between the k nearest neighbors and the new observation. Good performance of the new kNN classifier is demonstrated by three simulation studies and two real data examples. Y