Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

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

Full text not available from this repository.


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)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Publisher: IEEE
ISBN: 9781424437597
Last Modified: 29 Apr 2016 03:46

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item