An empirical study of non-binary genetic algorithm-based neural approaches for classification

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An empirical study of non-binary genetic algorithm-based neural approaches for classification

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International Conference on Information Systems archive
Proceedings of the 20th international conference on Information Systems table of contents

Charlotte, North Carolina, United States

Pages: 155 - 165

Year of Publication: 1999

ISBN:ICIS1999-X

Authors

Parag C. Pendharkar	School of Business Administration, Penn State University at Harrisburg
James A. Rodger	Eberly College of Business Administration, Indiana University of Pennsylvania

Sponsor

ACM: Association for Computing Machinery

Publisher

Association for Information Systems Atlanta, GA, USA

Bibliometrics

Downloads (6 Weeks): 3, Downloads (12 Months): 32, Citation Count: 2

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 2

	Rudy Setiono , Wee Kheng Leow , James Y. L. Thong, Opening the neural network black box: an algorithm for extracting rules from function approximating artificial neural networks, Proceedings of the twenty first international conference on Information systems, p.176-186, December 2000, Brisbane, Queensland, Australia
	Parag C. Pendharkar, A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem, Computers and Operations Research, v.32 n.10, p.2561-2582, October 2005