Abstract
This paper introduces a new algorithmic nature inspired approach that uses a hybridized Particle Swarm Optimization algorithm with a new neighborhood topology for successfully solving the Feature Selection Problem (FSP). The Feature Selection Problem is an interesting and important topic which is relevant for a variety of database applications. The proposed algorithm for the solution of the FSP, the Particle Swarm Optimization with Expanding Neighborhood Topology (PSOENT), combines a Particle Swarm Optimization (PSO) algorithm and the Variable Neighborhood Search (VNS) strategy. As, in general, the structure of the social network affects strongly a PSO algorithm, the proposed method by using an expanding neighborhood topology manages to increase the performance of the algorithm. As the algorithm starts from a small size neighborhood and by increasing (expanding) the size of the neighborhood, it ends to a neighborhood that includes all the swarm, it manages to take advantage of the exploration capabilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. In order to test the effectiveness and the efficiency of the proposed method we use data sets of different sizes and compare the proposed method with a number of other PSO algorithms and other algorithms from the literature.
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Marinakis, Y., Marinaki, M. (2013). A Hybridized Particle Swarm Optimization with Expanding Neighborhood Topology for the Feature Selection Problem. In: Blesa, M.J., Blum, C., Festa, P., Roli, A., Sampels, M. (eds) Hybrid Metaheuristics. HM 2013. Lecture Notes in Computer Science, vol 7919. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38516-2_4
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DOI: https://doi.org/10.1007/978-3-642-38516-2_4
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