Repositorio UVigo

A general framework for cost-sensitive boosting

Investigo Repository

A general framework for cost-sensitive boosting

Landesa Vazquez, Iago
 
DATE : 2014-07-28
UNIVERSAL IDENTIFIER : http://hdl.handle.net/11093/259
UNESCO SUBJECT : 1203.04 Inteligencia Artificial ; 1209.04 Teoría y Proceso de decisión
DOCUMENT TYPE : doctoralThesis

ABSTRACT :

Boosting algorithms have been widely used to tackle a plethora of problems. Among them, cost-sensitive classification stands out as one of the scenarios in which Boosting is most frequently applied in practice. In the last few years, a lot of approaches have been proposed in the literature to provide standard AdaBoost with asymmetric capabilities, each with a different focus. However, for the researcher, these algorithms shape a confusing heap with diffuse differences and properties, lacking a unified framework to jointly compare, classify, analyze and discuss the approaches on a common basis. Motivated by the preeminent role of AdaBoost in the Viola-Jones framework for object detection in images, a markedly asymmetric learning problem, in this thesis we try to untangle the different Cost-Sensitive AdaBoost alternatives presented in the literature, demystifying some preconceptions and making novel proposals (Cost- Generalized AdaBoost and AdaBoostDB) with a full theoretical derivation. We try to classify, analyze, compare and discuss this family of algorithms in order ... [+]
Boosting algorithms have been widely used to tackle a plethora of problems. Among them, cost-sensitive classification stands out as one of the scenarios in which Boosting is most frequently applied in practice. In the last few years, a lot of approaches have been proposed in the literature to provide standard AdaBoost with asymmetric capabilities, each with a different focus. However, for the researcher, these algorithms shape a confusing heap with diffuse differences and properties, lacking a unified framework to jointly compare, classify, analyze and discuss the approaches on a common basis. Motivated by the preeminent role of AdaBoost in the Viola-Jones framework for object detection in images, a markedly asymmetric learning problem, in this thesis we try to untangle the different Cost-Sensitive AdaBoost alternatives presented in the literature, demystifying some preconceptions and making novel proposals (Cost- Generalized AdaBoost and AdaBoostDB) with a full theoretical derivation. We try to classify, analyze, compare and discuss this family of algorithms in order to build a general framework unifying them. Our final goal is, thus, being able to find a definitive scheme to translate any cost-sensitive learning problem to the AdaBoost framework while shedding light on which algorithm ensures the best performance and formal guarantees. [-]

Show full item record



Files in this item

Attribution-NonCommercial-NoDerivs 3.0 Spain Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Spain
2013 Universidade de Vigo, Todos los derechos reservados
Calidad So9001