Many 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 using 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.
|Title of host publication||Proceedings - 6th IEEE International Conference on e-Science Workshops, e-ScienceW 2010|
|Number of pages||6|
|Publication status||Published - 2010|
|Event||6th IEEE International Conference on e-Science Workshops, e-ScienceW 2010 - Brisbane, QLD, Australia|
Duration: 7 Dec 2010 → 10 Dec 2010
|Conference||6th IEEE International Conference on e-Science Workshops, e-ScienceW 2010|
|Period||7/12/10 → 10/12/10|