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Evolutionary feature selection for artificial neural network pattern classifiers

Pham, D. T., Castellani, M. and Fahmy, Ashraf 2009. Evolutionary feature selection for artificial neural network pattern classifiers. Presented at: 7th IEEE International Conference on Industrial Informatics (INDIN2009), Cardiff, Wales, 23-26 June 2009. Industrial Informatics, 2009. INDIN 2009. 7th IEEE International Conference on. IEEE, pp. 658-663. 10.1109/INDIN.2009.5195881

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Abstract

This paper presents FeaSANNT, an evolutionary procedure for feature selection and weight training for neural network classifiers. FeaSANNT exploits the global nature of evolutionary search to avoid sub-optimal peaks of performance. FeaSANNT was used to train a multi-layer perceptron classifier on seven benchmark problems. FeaSANNT attained accurate and consistent learning results, and significantly reduced the number of data attributes compared to four state-of-the-art standard filter and wrapper feature selection methods. Thanks to the robustness of evolutionary search, FeaSANNT did not require time-consuming re-tuning of the learning parameters for each test problem.

Item Type: Conference or Workshop Item (Paper)
Status: Published
Schools: Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Engineering
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Publisher: IEEE
ISBN: 9781424437597
Last Modified: 29 Apr 2016 03:46
URI: http://orca-mwe.cf.ac.uk/id/eprint/69255

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