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A Comparative Study for Assessing the Reliability of Complex Networks Using Rules Extracted from Different Machine Learning Approaches

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AI 2005: Advances in Artificial Intelligence (AI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3809))

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Abstract

In this paper three machine learning approaches, Neural Networks (NN), Support Vector Machines (SVM) and Neural Fuzzy Networks (FuNN) are used to extract rules and assess the reliability of complex networks. For NN and SVM models the TREPAN approach is proposed as a valid tool for extracting rules whereas the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for tuning a previous set of rules derived by a fuzzy inference system and neural network approach.

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Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Central de Venezuela, Venezuela

    Douglas E. Torres D. & Claudio M. Rocco S.

  2. UNEFA, Núcleo Caracas,  

    Douglas E. Torres D.

Authors
  1. Douglas E. Torres D.
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  2. Claudio M. Rocco S.
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Editor information

Editors and Affiliations

  1. Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia

    Shichao Zhang

  2. Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia

    Ray Jarvis

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© 2005 Springer-Verlag Berlin Heidelberg

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Torres D., D.E., Rocco S., C.M. (2005). A Comparative Study for Assessing the Reliability of Complex Networks Using Rules Extracted from Different Machine Learning Approaches. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_119

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  • DOI: https://doi.org/10.1007/11589990_119

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30462-3

  • Online ISBN: 978-3-540-31652-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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