Nominal-scale evolving connectionist systems

Watts, Michael J.
Abstract
A method is presented for extending the evolving connectionist system (ECoS) algorithm that allows it to explicitly represent and learn nominal-scale data without the need for an orthogonal or binary encoding scheme. Rigorous evaluation of the algorithm over benchmark data sets shows that it is able to learn, generalise and adapt well to classification problems. The algorithm is potentially useful for data mining tasks.
Keywords
orthogonal encoding; learning (artificial intelligence); neural nets; binary encoding; data mining; evolving connectionist system algorithm; nominal-scale data
Date
2006-07
Type
Conference Contribution - Published (Conference Paper)
Collections
Share this
Thumbnail
View/Open
IJCNN.2006.246974.pdf
Metadata
 Expand record
© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Citation
Watts, M. J. (2006). Nominal-scale evolving connectionist systems. In 2006 International Joint Conference on Neural Networks, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16-21, 2006 (pp. 2055-2059). Piscataway, NJ: IEEE.