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Cybernetics And Systems Analysis
International Theoretical Science Journal
Volume 57 >>> № 6 NOVEMBER — DECEMBER 2021
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UDC 004.932.4
B.P. Rusyn1, O.A. Lutsyk2, R.Y. Kosarevych3


1 Karpenko Physico-Mechanical Institute
of the NAS of Ukraine, Lviv, Ukraine

 b.rusyn.prof@gmail.com

2 Karpenko Physico-Mechanical Institute
of the NAS of Ukraine, Lviv, Ukraine

 olutsyk@yahoo.com

3 Karpenko Physico-Mechanical Institute
of the NAS of Ukraine, Lviv, Ukraine

 kosar2311@gmail.com

EVALUATING THE INFORMATIVITY OF TRAINING SAMPLE
FOR CLASSIFICATION OF IMAGES BY DEEP LEARNING METHODS

Abstract. A new approach to evaluate the informativeness of the training sample when recognizing images obtained by means of remote sensing is proposed. It is shown that the informativeness of the training sample can be represented by a set of characteristics, each of which describes certain properties of the data. A relationship between the characteristics of the training sample and the accuracy of the classifier trained on the basis of this sample is established. The proposed approach is applied to various test training samples and the results of their evaluation are presented. When evaluating the training set by the proposed approach, the process is shown to be much faster than training a neural network. This makes it possible to use the proposed approach for preliminary estimation of the training sample in the problems of image recognition using deep learning methods.

Keywords: deep learning, feature selection, training sample, convolution network.


FULL TEXT

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