Combining independent and unbiased classifiers using weighted average

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2000

Autores

Luís Alexandre (Autor) Outra; Aurélio Campilho (Autor) FEUP; Mohamed Kamel (Autor) Outra

Ata de Conferência Internacional

Título: 15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS: PATTERN RECOGNITION AND NEURAL NETWORKS

Páginas: 495-498

15th International Conference on Pattern Recognition (ICPR-2000)

BARCELONA, SPAIN, SEP 03-07, 2000

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 22 Citações

Outras Informações

ID Authenticus: P-001-2BC

DOI: 10.1109/ICPR.2000.906120

Abstract (EN): In a classification problem, improved accuracy can be obtained in many situations by using the combination of several classifiers instead of a single one. In [10], the error reduction that can be obtained by combining unbiased classifiers with independent errors using a simple average, was derived. We present an extension of this result by finding the improvement obtained when combining classifiers using weighted average. We also prove that for unbiased classifiers with independent errors the best combination of N classifiers corresponds to a weighted average, where the combination coefficient of each classifier is equal to 1/N. This means that in these cases the simple average should be used. We present experiments illustrating our results.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4