A neuro-fuzzy architecture for high performance classification

Sung Bae Cho

doi:10.1007/3-540-60607-6_6

A neuro-fuzzy architecture for high performance classification

Sung Bae Cho

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The concept of combining modular neural networks has been recently exploited as a new direction for the development of highly reliable neural network systems in the area of pattern classification. In this paper we present an efficient method for combining the modular networks based on fuzzy logic, especially the fuzzy integral. This method nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. Also, for more effective aggregation, we adopt the extension of the fuzzy integral with ordered weighted averaging operators. The experimental results with the recognition problem of on-fine handwriting characters show that the performance of individual networks could be improved significantly.

Original language	English
Title of host publication	Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms - IEEE/Nagoya-University World Wisepersons Workshop, 1994, Selected Papers
Editors	Takeshi Furuhashi
Publisher	Springer Verlag
Pages	67-84
Number of pages	18
ISBN (Print)	9783540606079
DOIs	https://doi.org/10.1007/3-540-60607-6_6
Publication status	Published - 1995
Event	3rd World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms, WWW 1994 - Nagoya, Japan Duration: 1994 Aug 9 → 1994 Aug 10

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1011
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms, WWW 1994
Country	Japan
City	Nagoya
Period	94/8/9 → 94/8/10

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1995.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-60607-6_6

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Cho, S. B. (1995). A neuro-fuzzy architecture for high performance classification. In T. Furuhashi (Ed.), Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms - IEEE/Nagoya-University World Wisepersons Workshop, 1994, Selected Papers (pp. 67-84). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1011). Springer Verlag. https://doi.org/10.1007/3-540-60607-6_6

Cho, Sung Bae. / A neuro-fuzzy architecture for high performance classification. Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms - IEEE/Nagoya-University World Wisepersons Workshop, 1994, Selected Papers. editor / Takeshi Furuhashi. Springer Verlag, 1995. pp. 67-84 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A neuro-fuzzy architecture for high performance classification",

abstract = "The concept of combining modular neural networks has been recently exploited as a new direction for the development of highly reliable neural network systems in the area of pattern classification. In this paper we present an efficient method for combining the modular networks based on fuzzy logic, especially the fuzzy integral. This method nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. Also, for more effective aggregation, we adopt the extension of the fuzzy integral with ordered weighted averaging operators. The experimental results with the recognition problem of on-fine handwriting characters show that the performance of individual networks could be improved significantly.",

author = "Cho, {Sung Bae}",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1995.; 3rd World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms, WWW 1994 ; Conference date: 09-08-1994 Through 10-08-1994",

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Cho, SB 1995, A neuro-fuzzy architecture for high performance classification. in T Furuhashi (ed.), Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms - IEEE/Nagoya-University World Wisepersons Workshop, 1994, Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1011, Springer Verlag, pp. 67-84, 3rd World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms, WWW 1994, Nagoya, Japan, 94/8/9. https://doi.org/10.1007/3-540-60607-6_6

A neuro-fuzzy architecture for high performance classification. / Cho, Sung Bae.

Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms - IEEE/Nagoya-University World Wisepersons Workshop, 1994, Selected Papers. ed. / Takeshi Furuhashi. Springer Verlag, 1995. p. 67-84 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - The concept of combining modular neural networks has been recently exploited as a new direction for the development of highly reliable neural network systems in the area of pattern classification. In this paper we present an efficient method for combining the modular networks based on fuzzy logic, especially the fuzzy integral. This method nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. Also, for more effective aggregation, we adopt the extension of the fuzzy integral with ordered weighted averaging operators. The experimental results with the recognition problem of on-fine handwriting characters show that the performance of individual networks could be improved significantly.

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Cho SB. A neuro-fuzzy architecture for high performance classification. In Furuhashi T, editor, Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms - IEEE/Nagoya-University World Wisepersons Workshop, 1994, Selected Papers. Springer Verlag. 1995. p. 67-84. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-60607-6_6

A neuro-fuzzy architecture for high performance classification

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