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Attribute-Based Decision Graphs and Their Roles in Machine Learning Related Tasks

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Advances in Feature Selection for Data and Pattern Recognition

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 138))

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Abstract

Recently, new supervised machine learning algorithm has been proposed which is heavily supported by the construction of an attribute-based decision graph (AbDG) structure, for representing, in a condensed way, the training set associated with a learning task. Such structure has been successfully used for the purposes of classification and imputation in both, stationary and non-stationary environments. This chapter provides a detailed presentation of the motivations and main technicalities involved in the process of constructing AbDGs, as well as stresses some of the strengths of this graph-based structure, such as robustness and low computational costs associated with both, training and memory use. Given a training set, a collection of algorithms for constructing a weighted graph (i.e., an AbDG) based on such data is presented. The chapter describes in details algorithms involved in creating the set of vertices, the set of edges and, also, assigning labels to vertices and weights to edges. Ad-hoc algorithms for using AbDGs for both, classification or imputation purposes, are also addressed.

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Author information

Authors and Affiliations

  1. School of Technology, University of Campinas, R. Paschoal Marmo 1888, Jd. Nova Itália, Limeira, SP, 13484-332, Brazil

    João Roberto Bertini Junior

  2. Campo Limpo Paulista School, R. Guatemala 167, Campo Limpo Paulista, SP, 13231-230, Brazil

    Maria do Carmo Nicoletti

  3. Computer Science Department, Federal University of São Carlos, Rodovia Washington Luís s/n, São Carlos, SP, 13565-905, Brazil

    Maria do Carmo Nicoletti

Authors
  1. João Roberto Bertini Junior
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  2. Maria do Carmo Nicoletti
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Correspondence to João Roberto Bertini Junior .

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Editors and Affiliations

  1. Silesian University of Technology , Gliwice, Poland

    Urszula Stańczyk

  2. University of Silesia in Katowice , Katowice, Poland

    Beata Zielosko

  3. University of Bournemouth , Poole, United Kingdom

    Lakhmi C. Jain

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Bertini Junior, J.R., Nicoletti, M.d.C. (2018). Attribute-Based Decision Graphs and Their Roles in Machine Learning Related Tasks. In: Stańczyk, U., Zielosko, B., Jain, L. (eds) Advances in Feature Selection for Data and Pattern Recognition. Intelligent Systems Reference Library, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-319-67588-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-67588-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67587-9

  • Online ISBN: 978-3-319-67588-6

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