Feature selection for classification using an ant colony systeme-Science 2010: Sixth IEEE international conference on e-Science
Date of this Version12-7-2010
Document TypeConference Paper
AbstractMany applications such as pattern recognition require selecting a subset of the input features in order to represent the whole set of features. The aim of feature selection is to remove irrelevant or redundant features while keeping the most informative ones. In this paper, an ant colony system approach for solving feature selection for classification is presented. The proposed algorithm was tested rising artificial and real-world datasets. The results are promising in terms of the accuracy of the classifier and the number of selected features in all the used datasets. The results of the proposed algorithm have been compared with other results available in the literature and found to be favorable.
Citation InformationNadia Abd-Alsabour and Marcus Randall. "Feature selection for classification using an ant colony system" e-Science 2010: Sixth IEEE international conference on e-Science (2010) p. 86 - 91
Available at: http://works.bepress.com/marcus_randall/33/