On the consistency of multiclass classification methods


Autoria(s): Tewari, Ambuj; Bartlett, Peter L.
Data(s)

01/05/2007

Resumo

Binary classification is a well studied special case of the classification problem. Statistical properties of binary classifiers, such as consistency, have been investigated in a variety of settings. Binary classification methods can be generalized in many ways to handle multiple classes. It turns out that one can lose consistency in generalizing a binary classification method to deal with multiple classes. We study a rich family of multiclass methods and provide a necessary and sufficient condition for their consistency. We illustrate our approach by applying it to some multiclass methods proposed in the literature.

Identificador

http://eprints.qut.edu.au/44000/

Publicador

Journal of Machine Learning Research

Relação

http://www.jmlr.org/papers/volume8/tewari07a/tewari07a.pdf

Tewari, Ambuj & Bartlett, Peter L. (2007) On the consistency of multiclass classification methods. Journal of Machine Learning Research, 8, pp. 1007-1025.

Direitos

Copyright 2007 Journal of Machine Learning Research

Fonte

Faculty of Science and Technology; Mathematical Sciences

Palavras-Chave #080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING #170200 COGNITIVE SCIENCE #multiclass classification #consistency #Bayes risk #OAVJ
Tipo

Journal Article