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Extending Flow Graphs for Handling Continuous−Valued Attributes

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Hybrid Intelligent Systems (HIS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 923))

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Abstract

This paper describes a research work that focuses on a suitable data structure, capable of summarizing a given supervised training data set into a weighted and labeled digraph. The data structure, named flow graph (FG), has been proposed not only for representing a set of supervised training data aiming at its analysis but, also, for supporting the extraction of decision rules, aiming at a classifier. The work described in this paper extends the original FG, suitable for discrete data, for dealing with continuous data. The extension is implemented as a discretization process, carried out as a pre-processing step previously to learning, in a two-step hybrid approach named HFG (Hybrid FG). The results of the conducted experiments were analyzed with focus on both, the induced graph-based structure and the performance of the associated set of rules extracted from the structure. Results obtained using the J48 are also presented, for comparison purposes.

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Acknowledgments

The authors thank UNIFACCAMP and CNPq for their support and the anonymous reviewers for their suggestions. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.

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Authors and Affiliations

  1. Centro Universitário C. Limpo Paulista (UNIFACCAMP), Campo Limpo Paulista, SP, Brazil

    Emilio Carlos Rodrigues & Maria do Carmo Nicoletti

  2. Instituto Federal de São Paulo (IFSP), Bragança Paulista, SP, Brazil

    Emilio Carlos Rodrigues

  3. Universidade Federal de S. Carlos (UFSCar), São Carlos, SP, Brazil

    Maria do Carmo Nicoletti

Authors
  1. Emilio Carlos Rodrigues
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  2. Maria do Carmo Nicoletti
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Corresponding author

Correspondence to Emilio Carlos Rodrigues .

Editor information

Editors and Affiliations

  1. School of Engineering, Instituto Superior de Engenharia (ISEP/IPP), Porto, Portugal

    Ana Maria Madureira

  2. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  3. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  4. Department of Production and Systems, University of Minho, Guimarães, Portugal

    Maria Leonilde Varela

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Rodrigues, E.C., Nicoletti, M.d.C. (2020). Extending Flow Graphs for Handling Continuous−Valued Attributes. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_6

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