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Algorithms for Attribute Selection and Knowledge Discovery

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Knowledge Management in Organizations (KMO 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 731))

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Abstract

The features relevant selection is a task performed prior to the data mining and can be seen as one of the most important problems to solve in the data preprocessing stage an in the machine learning. With the feature selection is mainly intended to improve predictive or descriptive performance of models and implement faster and less expensive algorithms. In this paper an analysis about feature selection methods is made emphasizing on decision trees, entropy measure for ranking features, and estimation of distribution algorithms. Finally, we show the result analysis of execute the three algorithms.

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Author information

Authors and Affiliations

  1. District University “Francisco José de Caldas”, Bogotá, Colombia

    Jorge Enrique Rodríguez R., Víctor Hugo Medina García & Lina María Medina Estrada

Authors
  1. Jorge Enrique Rodríguez R.
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  2. Víctor Hugo Medina García
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  3. Lina María Medina Estrada
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Corresponding authors

Correspondence to Jorge Enrique Rodríguez R. , Víctor Hugo Medina García or Lina María Medina Estrada .

Editor information

Editors and Affiliations

  1. University of Staffordshire, Stoke-on-Trent, Staffordshire, United Kingdom

    Lorna Uden

  2. Beijing Jiaotong University, Beijing, China

    Wei Lu

  3. Department of Information Management, University of Kaohsiung, Kaohsiung, Taiwan

    I-Hsien Ting

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Rodríguez R., J.E., García, V.H.M., Estrada, L.M.M. (2017). Algorithms for Attribute Selection and Knowledge Discovery. In: Uden, L., Lu, W., Ting, IH. (eds) Knowledge Management in Organizations. KMO 2017. Communications in Computer and Information Science, vol 731. Springer, Cham. https://doi.org/10.1007/978-3-319-62698-7_33

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  • DOI: https://doi.org/10.1007/978-3-319-62698-7_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62697-0

  • Online ISBN: 978-3-319-62698-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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