Abstract
The set feature selection methods based on the paradigms of computational intelligence (evolutionary search and swarm intelligence) is proposed. Proposed methods speed up the search through the creation of special operators, taking into account a priori information about the data sample and concentrating search on the most perspective solution areas. This allows preserving the stochastic nature of the search to accelerate the obtainment of acceptable solutions through the introduction of deterministic component in the search strategy. The theoretical estimates of the computational (temporal) and spatial complexity of the developed methods are obtained. The proposed methods are experimentally studied on a set of problems of automatic object classification, technical and medical diagnosis. On the results of experiments the comparative characteristics and recommendations for the use of the proposed methods are given.
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Acknowledgements
This paper is prepared with partial support of “Centers of Excellence for young RESearchers” (CERES) project (Reference Number 544137-TEMPUS-1-2013-1-SK-TEMPUS-JPHES) of Tempus Programme of the European Union.
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Subbotin, S.A., Oliinyk, A.A. (2017). The Dimensionality Reduction Methods Based on Computational Intelligence in Problems of Object Classification and Diagnosis. In: Szewczyk, R., Kaliczyńska, M. (eds) Recent Advances in Systems, Control and Information Technology. SCIT 2016. Advances in Intelligent Systems and Computing, vol 543. Springer, Cham. https://doi.org/10.1007/978-3-319-48923-0_2
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