A comparison of ranking methods for classification algorithm selection

• Tipo: Artigo em Revista Científica Internacional
• Data: 2000

Autores

Brazdil, PB (Autor) FEP; Soares, C (Autor) FEP

Revista

Título: Lecture Notes in Computer Science

Vol. 1810

Páginas: 63-74

ISSN: 0302-9743

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 109 Citações

Scopus - 138 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-001-1VS

DOI: 10.1007/3-540-45164-1_8

Abstract (EN): We investigate the problem of using past performance information to select an algorithm for a given classification problem. We present three ranking methods for that purpose: average ranks, success rate ratios and significant wins. We also analyze the problem of evaluating and comparing these methods. The evaluation technique used is based on a leave-one-out procedure. On each iteration, the method generates a ranking using the results obtained by the algorithms on the training datasets. This ranking is then evaluated by calculating its distance from the ideal ranking built using the performance information on the test dataset. The distance measure adopted here, average correlation, is based on Spearman's rank correlation coefficient. To compare ranking methods, a combination of Friedman's test and Dunn's multiple comparison procedure is adopted. When applied to the methods presented here, these tests indicate that the success rate ratios and average ranks methods perform better than significant wins.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12