CHOICE OF NEIGHBOR ORDER IN NEAREST-NEIGHBOR CLASSIFICATION

• 成果类型: Article
• 署名作者: Hall, Peter; Park, Byeong U.; Samworth, Richard J.
• 署名单位: University of Melbourne; Seoul National University (SNU); University of Cambridge
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/07-AOS537
• 发表日期: 2008
• 页码: 2135-2152
• 关键词: nonparametric discrimination; pattern-classification; CONVERGENCE; error; rates; probability; Consistency; CLASSIFIERS; rules; RISK

摘要

The kth-nearest neighbor rule is arguably the simplest and most intuitively appealing nonparametric classification procedure. However, application of this method is inhibited by lack of knowledge about its properties, in particular, about the manner in which it is influenced by the value of k; and by the absence of techniques for empirical choice of k. In the present paper we detail the way in which the value of k determines the misclassification error. We consider two models, Poisson and Binomial, for the training samples. Under the first model, data are recorded in a Poisson stream and are assigned to one or other of the two populations in accordance with the prior probabilities. In particular, the total number of data in both training samples is a Poisson-distributed random variable. Under the Binomial model, however, the total number of data in the training samples is fixed, although again each data value is assigned in a random way. Although the values of risk and regret associated with the Poisson and Binomial models are different, they are asymptotically equivalent to first order, and also to the risks associated with kernel-based classifiers that are tailored to the case of two derivatives. These properties motivate new methods for choosing the value of k.