Context. The task of automation of big data reduction in diagnostics and pattern recognition problems is solved. The object of the<br />research is the process of big data reduction. The subject of the research are the methods of big data reduction.<br />Objective. The research objective is to develop parallel method of big data reduction based on stochastic calculations.<br />Method. The parallel method of big data reduction is proposed. This method is based on the proposed criteria system, which allows to<br />estimate concentration of control points around local extrema. Calculation of solution concentration estimates in the developed criteria<br />system is based on the spatial location of control points in the current solution set. The proposed criteria system can be used in stochastic<br />search methods to monitor situations of excessive solution concentration in the areas of local optima and, as a consequence, to increase the diversity of the solution set in the current population and to cover the search space by control points in a more uniform way during<br />optimization process.<br />Results. The software which implements the proposed parallel method of big data reduction and allows to select informative features<br />and to reduce the big data for synthesis of recognition models based on the given data samples has been developed.<br />Conclusions. The conducted experiments have confirmed operability of the proposed parallel method of big data reduction and allow<br />to recommend it for processing of data sets for pattern recognition in practice. The prospects for further researches may include the<br />modification of the known feature selection methods and the development of new ones based on the proposed system of criteria for control points concentration estimation.
Науковий журнал «Радіоелектроніка, інформатика, управління»
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Oliinyk, A.; Zaporizhzhia National Technical University,
Zaporizhzhia, Ukraine Subbotin, S.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine Lovkin, V.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine Ilyashenko, M.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine Blagodariov, O.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine |
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Context. The task of automation of big data reduction in diagnostics and pattern recognition problems is solved. The object of the<br />research is the process of big data reduction. The subject of the research are the methods of big data reduction.<br />Objective. The research objective is to develop parallel method of big data reduction based on stochastic calculations.<br />Method. The parallel method of big data reduction is proposed. This method is based on the proposed criteria system, which allows to<br />estimate concentration of control points around local extrema. Calculation of solution concentration estimates in the developed criteria<br />system is based on the spatial location of control points in the current solution set. The proposed criteria system can be used in stochastic<br />search methods to monitor situations of excessive solution concentration in the areas of local optima and, as a consequence, to increase the diversity of the solution set in the current population and to cover the search space by control points in a more uniform way during<br />optimization process.<br />Results. The software which implements the proposed parallel method of big data reduction and allows to select informative features<br />and to reduce the big data for synthesis of recognition models based on the given data samples has been developed.<br />Conclusions. The conducted experiments have confirmed operability of the proposed parallel method of big data reduction and allow<br />to recommend it for processing of data sets for pattern recognition in practice. The prospects for further researches may include the<br />modification of the known feature selection methods and the development of new ones based on the proposed system of criteria for control points concentration estimation. |
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Zaporizhzhya National Technical University 2018-10-04 12:10:39 |
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application/pdf http://ric.zntu.edu.ua/article/view/142950 |
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Radio Electronics, Computer Science, Control; No 2 (2018): Radio Electronics, Computer Science, Control |
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Copyright (c) 2018 A. Oliinyk, S. Subbotin, V. Lovkin, M. Ilyashenko, O. Blagodariov |
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