Un Estudio de Métodos de Aprendizaje con Clases Desbalanceadas en la Inspección Visual Automática (A Survey on Class Imbalance Learning on Automatic Visual Inspection)

Carlos Mera (camerab@unal.edu.co), John William Branch (jwbranch@unal.edu.co)

Universidad Nacional de Colombia, Sede Medellín
This paper appears in: Revista IEEE América Latina

Publication Date: June 2014
Volume: 12,   Issue: 4 
ISSN: 1548-0992

The supervised machine learning has been showing very useful for the automatic visual inspection task. However, little has been considered about use traditional machine learning techniques on a domain where the classes are imbalanced. This problem corresponds to dealing with the situation where one class outnumbers the other. Traditional machine learning algorithms trained with imbalance datasets can be biased towards the majority class, thus producing poor predictive accuracy over the minority class. In this paper, we present different approaches to address the class imbalance problem and how these approaches have been used in the context of automatic visual inspection. The literature shows there are few works that consider the class imbalance problem on automatic visual inspection task and it shows that the one class classification technique is the most used.

Index Terms:
Automatic visual inspection, Class imbalance learning, Classifiers ensemble, Cost-sensitive learning, One Class Classification   

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