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A hybridized particle swarm optimization with expanding neighborhood topology for the feature selection problem

Marinaki Magdalini, Marinakis Ioannis

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/97E2BD78-AEF5-42A1-B63A-016B48CF1B80
Έτος 2013
Τύπος Περίληψη Δημοσίευσης σε Συνέδριο
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά 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
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Περίληψη

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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