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A Feature Selection Algorithm Based on Heuristic Decomposition

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Progress in Artificial Intelligence (EPIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10423))

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Abstract

Feature selection is one of the most important concepts in data mining when dimensionality reduction is needed. The performance measures of feature selection encompass predictive accuracy and result comprehensibility. Consistency based feature selection is a significant category of feature selection research that substantially improves the comprehensibility of the result using the parsimony principle. In this work, the feature selection algorithm LAID, Logical Analysis of Inconsistent Data, is applied to large volumes of data. In order to deal with hundreds of thousands of attributes, a problem de-composition strategy associated with a set covering problem formulation is used. The algorithm is applied to artificial datasets with genome-like characteristics of patients with rare diseases.

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Acknowledgements

The first author would like to thank the FCT support UID/Multi/04046/2013. This work used the EGI infrastructure with the support of NCG-INGRID-PT (Portugal) and BIFI (Spain).

Author information

Authors and Affiliations

  1. MAS-BioISI, FCUL and Universidade Aberta, Lisbon, Portugal

    Luís Cavique

  2. Universidade dos Açores, Ponta Delgada, Portugal

    Armando B. Mendes

  3. BioISI, FCUL and Instituto Dr. Ricardo Jorge, Lisbon, Portugal

    Hugo F. M. C. Martiniano

Authors
  1. Luís Cavique
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  2. Armando B. Mendes
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  3. Hugo F. M. C. Martiniano
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Corresponding author

Correspondence to Luís Cavique .

Editor information

Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    Eugénio Oliveira

  2. Universidade do Porto, Porto, Portugal

    João Gama

  3. Polytechnic Institute of Porto, Porto, Portugal

    Zita Vale

  4. Universidade do Porto, Porto, Portugal

    Henrique Lopes Cardoso

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Cavique, L., Mendes, A.B., Martiniano, H.F.M.C. (2017). A Feature Selection Algorithm Based on Heuristic Decomposition. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_43

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  • DOI: https://doi.org/10.1007/978-3-319-65340-2_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65339-6

  • Online ISBN: 978-3-319-65340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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