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Reliable Attribute Selection Based on Random Forest (RASER)

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Book cover Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

Feature selection has become one of the most active research areas in the field of data mining. It allows removing redundant and irrelevant data sets of large size. Furthermore, there are several methods in the literature for selecting attributes. In this article, a new multi-objective method is proposed to select relevant and non-redundant features. Our proposed feature selection method is divided into three stages: The first step computes the feature relevance value based on random forests. The second step, computes the dissimilarity matrix representing the dependence between the features of our training datasets, and transform it into a complete graph whose nodes represent features and edges represent the values of dissimilarities between them. The last step is for the optimization in which a multi-objective optimization algorithm is applied. The proposed method is applied on many datasets to find the most relevant and non-redundant features and the performance of the proposed method is compared with that of the popular MBEGA, mRMR (MIQ) and mRMR (MID).

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Monastir, Monastir, Tunisia

    Aboudi Noura & Hechmi Shili

  2. Department of Computer Science, University of Sousse, Sousse, Tunisia

    Lotfi Ben Romdhane

Authors
  1. Aboudi Noura
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  2. Hechmi Shili
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  3. Lotfi Ben Romdhane
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Corresponding author

Correspondence to Aboudi Noura .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Noura, A., Shili, H., Romdhane, L.B. (2017). Reliable Attribute Selection Based on Random Forest (RASER). In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

  • eBook Packages: EngineeringEngineering (R0)

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