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)
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IJCNN.2006.246974.pdf
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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.