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Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/97E2BD78-AEF5-42A1-B63A-016B48CF1B80 | ||
| Year | 2013 | ||
| Type of Item | Conference Paper Abstract | ||
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| Bibliographic Citation | Y. Marinakis and M. Marinaki, “A Hybridized Particle Swarm Optimization with Expanding Neighborhood Topology for the Feature Selection Problem”, in 8th International Workshop, 2013, pp. 37-51. doi: 10.1007/978-3-642-38516-2_4 https://doi.org/10.1007/978-3-642-38516-2_4 | ||
| Appears in Collections |
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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