Robust asymmetric Adaboost

Pablo Ormeño, Felipe Ramírez, Carlos Valle, HÉCTOR GABRIEL ALLENDE CID, Héctor Allende

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

In real world pattern recognition problems, such as computer-assisted medical diagnosis, events of a given phenomena are usually found in minority, making it necessary to build algorithms that emphasize the effect of one of the classes at training time. In this paper we propose a variation of the well-known Adaboost algorithm that is able to improve its performance by using an asymmetric and robust cost function. We assess the performance of the proposed method on two medical datasets and synthetic datasets with different levels of imbalance and compare our results against three state-of-the-art ensemble learning approaches, achieving better and comparable results.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 17th Iberoamerican Congress, CIARP 2012, Proceedings
Páginas519-526
Número de páginas8
DOI
EstadoPublicada - 2012
Publicado de forma externa
Evento17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012 - Buenos Aires, Argentina
Duración: 3 sep 20126 sep 2012

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen7441 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012
País/TerritorioArgentina
CiudadBuenos Aires
Período3/09/126/09/12

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