A note on genetic algorithms for large-scale feature selection
作者： W. Siedlecki , J. Sklansky
DOI: 10.1016/0167-8655(89)90037-8
关键词:
摘要: Abstract We introduce the use of genetic algorithms (GA) for selection features in design automatic pattern classifiers. Our preliminary results suggest that GA is a powerful means reducing time finding near-optimal subsets from large sets.
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A.K. Jain, B. Chandrasekaran, 39 Dimensionality and sample size considerations in pattern recognition practice Handbook of Statistics. ,vol. 2, pp. 835- 855 ,(1982) , 10.1016/S0169-7161(82)02042-2
Jack Sklansky, Gustav N. Wassel, Pattern Classifiers and Trainable Machines ,(1981)
John H. Holland, Adaptation in natural and artificial systems ,(1992)
Narendra, Fukunaga, A Branch and Bound Algorithm for Feature Subset Selection IEEE Transactions on Computers. ,vol. 26, pp. 917- 922 ,(1977) , 10.1109/TC.1977.1674939
Josef Kittler, Pierre A. Devijver, Pattern recognition : a statistical approach Prentice/Hall International. ,(1982)
Iman Foroutan, Jack Sklansky, Feature Selection for Automatic Classification of Non-Gaussian Data systems man and cybernetics. ,vol. 17, pp. 187- 198 ,(1987) , 10.1109/TSMC.1987.4309029
WOJCIECH SIEDLECKI, JACK SKLANSKY, ON AUTOMATIC FEATURE SELECTION International Journal of Pattern Recognition and Artificial Intelligence. ,vol. 02, pp. 197- 220 ,(1988) , 10.1142/S0218001488000145
David E. Goldberg, Genetic algorithms in search, optimization and machine learning Reading: Addison-Wesley. ,(1989)