Abstract
This paper presents an efficient feature selection algorithm by utilizing the strategy of ant colony optimization, called as ACOFS. Initially, ACOFS uses a modified framework to guide the ants in the right directions while constructing the graph (subset) paths. In the subsequent part, a set of new modified pheromone update rules as well as a set of new modified estimation of heuristic information for features are introduced. The effect of such modifications ultimately assists ants to generate salient feature subsets with reduced size. We evaluate the performance of ACOFS on four real-world benchmark datasets. The experimental results show that ACOFS has a remarkable capability to generate reduced size subsets of salient features with yielding significant classification accuracies.
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Kabir, M.M., Shahjahan, M., Murase, K. (2009). An Efficient Feature Selection Using Ant Colony Optimization Algorithm. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_27
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DOI: https://doi.org/10.1007/978-3-642-10684-2_27
Publisher Name: Springer, Berlin, Heidelberg
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