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Deleting or Keeping Outliers for Classifier Training?

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Autor: Tallón Ballesteros, Antonio Javier
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2014
Publicado en: Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC), 2014 (2014), 281-286
ISBN/ISSN: 978-1-4799-5936-5
Tipo de documento: Ponencia
Resumen: This paper introduces two statistical outlier detection approaches by classes. Experiments on binary and multi-class classification problems reveal that the partial removal of outliers improves significantly one or two performance measures for C4.S and I-nearest neighbour classifiers. Also, a taxonomy of problems according to the amount of outliers is proposed.
Cita: Tallón Ballesteros, A.J. y Riquelme Santos, J.C. (2014). Deleting or Keeping Outliers for Classifier Training?. En Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC), 2014 (281-286), Porto (Portugal): IEEE.
Tamaño: 564.1Kb
Formato: PDF

URI: http://hdl.handle.net/11441/42708

DOI: http://dx.doi.org/10.1109/NaBIC.2014.6921892

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