skip to main content
Visitante
Meu Espaço
Minha Conta
Sair
Identificação
This feature requires javascript
Tags
Revistas Eletrônicas (eJournals)
Livros Eletrônicos (eBooks)
Bases de Dados
Bibliotecas USP
Ajuda
Ajuda
Idioma:
Inglês
Espanhol
Português
This feature required javascript
This feature requires javascript
Primo Search
Busca Geral
Busca Geral
Acervo Físico
Acervo Físico
Produção Intelectual da USP
Produção USP
Search For:
Clear Search Box
Search in:
Busca Geral
Or hit Enter to replace search target
Or select another collection:
Search in:
Busca Geral
Busca Avançada
Busca por Índices
This feature requires javascript
This feature requires javascript
Error estimation based on variance analysis of k-fold cross-validation
Jiang, Gaoxia ; Wang, Wenjian
Pattern recognition, 2017-09, Vol.69, p.94-106
[Periódico revisado por pares]
Elsevier Ltd
Texto completo disponível
Citações
Citado por
Exibir Online
Detalhes
Resenhas & Tags
Mais Opções
Nº de Citações
This feature requires javascript
Enviar para
Adicionar ao Meu Espaço
Remover do Meu Espaço
E-mail (máximo 30 registros por vez)
Imprimir
Link permanente
Referência
EasyBib
EndNote
RefWorks
del.icio.us
Exportar RIS
Exportar BibTeX
This feature requires javascript
Título:
Error estimation based on variance analysis of k-fold cross-validation
Autor:
Jiang, Gaoxia
;
Wang, Wenjian
Assuntos:
Error estimation
;
k-fold cross-validation
;
Model selection
;
Variance analysis
É parte de:
Pattern recognition, 2017-09, Vol.69, p.94-106
Descrição:
•When the numbers of samples and folds are both large enough, we proved that CV variance and its accuracy have the quadratic relationship.•The relationships between CV variance and its factors have been derived, allowing to predict which variance is less before applying k-fold CV.•Theoretical explanations have been given for some empirical evidences of Rodriguez and Kohavi from the respect of variance analysis.•The proposed normalized variance has significant correlation with the error and is unrelated to k so that it can serve as a stable error measurement. Cross-validation (CV) is often used to estimate the generalization capability of a learning model. The variance of CV error has a considerable impact on the accuracy of CV estimator and the adequacy of the learning model, so it is very important to analyze CV variance. The aim of this paper is to investigate how to improve the accuracy of the error estimation based on variance analysis. We first describe the quantitative relationship between CV variance and its accuracy, which can provide guidance for improving the accuracy by reducing the variance. We then study the relationships between variance and relevant variables including the sample size, the number of folds, and the number of repetitions. These form the basis of theoretical strategies of regulating CV variance. Our classification results can theoretically explain the empirical results of Rodríguez and Kohavi. Finally, we propose a uniform normalized variance which not only measures model accuracy but also is irrelative to fold number. Therefore, it simplifies the selection of fold number in k-fold CV and normalized variance can serve as a stable error measurement for model comparison and selection. We report the results of experiments using 5 supervised learning models and 20 datasets. The results indicate that it is reliable to determine which variance is less before k-fold CV by the proposed theorems, and thus the accuracy of error estimation can be promoted by reducing variance. In so doing, we are more likely to select the best parameter or model.
Editor:
Elsevier Ltd
Idioma:
Inglês
This feature requires javascript
This feature requires javascript
Voltar para lista de resultados
This feature requires javascript
This feature requires javascript
Buscando em bases de dados remotas. Favor aguardar.
Buscando por
em
scope:(USP_VIDEOS),scope:("PRIMO"),scope:(USP_FISICO),scope:(USP_EREVISTAS),scope:(USP),scope:(USP_EBOOKS),scope:(USP_PRODUCAO),primo_central_multiple_fe
Mostrar o que foi encontrado até o momento
This feature requires javascript
This feature requires javascript