Data classification based on fuzzy-RBF networks

A. Várkonyi-Kóczy, B. Tusor, J. Bukor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Classification has been among the most typical computational problems in the last decades. In this paper, a new filtering network is proposed for data classification that is derived from radial base function networks (RBFNs), based on a simple observation about the nature of the classic RBFNs. According to that observation, the hidden layer of the network can be viewed as a fuzzy system, which compares the input data to the data stored in each neuron, computing the similarity between them. The output layer of the RBFN is modified in order to make it more effective in certain fuzzy decision-making systems. The training of the neurons is solved by a clustering step, for which a novel clustering method is proposed. Experimental results are also presented to show the efficiency of the approach.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Pages829-840
Number of pages12
Volume357
ISBN (Print)9783319184159
DOIs
Publication statusPublished - 2016
Event6th International Workshop Soft Computing Applications, SOFA 2014 - Timisoara
Duration: Jul 24 2014Jul 26 2014

Publication series

NameAdvances in Intelligent Systems and Computing
Volume357
ISSN (Print)21945357

Other

Other6th International Workshop Soft Computing Applications, SOFA 2014
CityTimisoara
Period7/24/147/26/14

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Keywords

  • Classification
  • Clustering
  • Fuzzy decision-making
  • Radial base function networks
  • Supervised machine learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Várkonyi-Kóczy, A., Tusor, B., & Bukor, J. (2016). Data classification based on fuzzy-RBF networks. In Advances in Intelligent Systems and Computing (Vol. 357, pp. 829-840). (Advances in Intelligent Systems and Computing; Vol. 357). Springer Verlag. https://doi.org/10.1007/978-3-319-18416-6_65