Abstract
Feature Extraction (FE) and Feature Selection (FS) are the most important steps in classification systems. One approach in the feature selection area is employing population-based optimization algorithms such as Particle Swarm Optimization (PSO)-based method and Ant Colony Optimization (ACO)-based method. This paper presents a novel feature selection method that is based on Ant Colony Optimization (ACO). This approach is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of standard binary PSO algorithm on the task of feature selection in Postsynaptic dataset. Simulation results on Postsynaptic dataset show the superiority of the proposed algorithm.
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Basiri, M.E., Ghasem-Aghaee, N., Aghdam, M.H. (2008). Using Ant Colony Optimization-Based Selected Features for Predicting Post-synaptic Activity in Proteins. In: Marchiori, E., Moore, J.H. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2008. Lecture Notes in Computer Science, vol 4973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78757-0_2
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DOI: https://doi.org/10.1007/978-3-540-78757-0_2
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