Combining Multiple Neural Networks by Fuzzy Integral for Robust Classification

Sung-Bae Cho, Jin H. Kim

Research output: Contribution to journalArticle

276 Citations (Scopus)

Abstract

Recently, in the area of artificial neural networks, the concept of combining multiple networks has been proposed as a new direction for the development of highly reliable neural network systems. In this paper we propose a method for multinetwork combination based on the fuzzy integral. This technique nonlinearly combines objective evidence, in the form of a fuzzy membership function, with subjective evaluation of the worth of the individual neural networks with respect to the decision. The experimental results with the recognition problem of on-line handwriting characters confirm the superiority of the presented method to the other voting techniques.

Original languageEnglish
Pages (from-to)380-384
Number of pages5
JournalIEEE Transactions on Systems, Man and Cybernetics
Volume25
Issue number2
DOIs
Publication statusPublished - 1995 Jan 1

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Neural networks
Membership functions

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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Combining Multiple Neural Networks by Fuzzy Integral for Robust Classification. / Cho, Sung-Bae; Kim, Jin H.

In: IEEE Transactions on Systems, Man and Cybernetics, Vol. 25, No. 2, 01.01.1995, p. 380-384.

Research output: Contribution to journalArticle

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