Title: A new knn-classifier for functional data with applications
Time:19:00--16:00, Monday, December 9, 2019
Location:X2511
Abstract:In this talk, we discuss a new knn (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 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 knnclassifier is demonstrated by simulation studies and real data examples.
