A comparative study for assessing the reliability of complex networks using rules extracted from different machine learning approaches

Authors:
Douglas E. Torres D.;Claudio M. Rocco S.
Affiliations:
Facultad de Ingeniería, Universidad Central de Venezuela, Venezuela;Facultad de Ingeniería, Universidad Central de Venezuela, Venezuela
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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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.