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The Imbalanced Problem in Morphological Galaxy Classification

  • Jorge de la Calleja
  • Gladis Huerta
  • Olac Fuentes
  • Antonio Benitez
  • Eduardo López Domínguez
  • Ma. Auxilio Medina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)

Abstract

In this paper we present an experimental study of the performance of six machine learning algorithms applied to morphological galaxy classification. We also address the learning approach from imbalanced data sets, inherent to many real-world applications, such as astronomical data analysis problems. We used two over-sampling techniques: SMOTE and Resampling, and we vary the amount of generated instances for classification. Our experimental results show that the learning method Random Forest with Resampling obtain the best results for three, five and seven galaxy types, with a F-measure about .99 for all cases.

Keywords

machine learning imbalanced data sets galaxies 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jorge de la Calleja
    • 1
  • Gladis Huerta
    • 1
  • Olac Fuentes
    • 2
  • Antonio Benitez
    • 1
  • Eduardo López Domínguez
    • 1
  • Ma. Auxilio Medina
    • 1
  1. 1.Ingeniería en InformáticaUniversidad Politécnica de PueblaPueblaMéxico
  2. 2.Computer Science DepartmentUniversity of Texas at El PasoU.S.A.

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