Localization of vectors–patterns in the problems of parametric classification with the purpose of increasing its accuracy

Authors

  • Mourad Aouati Central City Police Department of Constantine Ali Mendjeli UV 01 Ilot 03 Bt H n°123 Constantine Algeria, Algeria

DOI:

https://doi.org/10.15587/1729-4061.2016.76171

Keywords:

parametric classification, covariance matrix, pattern recognition, accuracy of classification of objects, vector-pattern, cluster

Abstract

The problem of the accuracy of classification, determined by the share of the correctly classified objects with the use of parametric methods of pattern recognition, is examined. In particular, we analyzed the influence of deviation from the normal law of distribution of variables of the space of features – random variables, in essence – and the inequality of covariance matrices of classes on the accuracy of classification.  It was found that the inequality of covariance matrices of the divided classes leads to the shift of the dividing surface; however, the magnitude of this displacement may not become the essential factor, which influences the accuracy of classification. It is shown for the space of variables of dimensionality (N×2) that for the randomly selected classes inside the square with the length of the edge, equal to two, the accuracy of classification for classes A and B proves to be different and depends on the position of the straight line, which divides the classes. It is shown that localization of vectors-patterns in the space of features can be selected in accordance with the plans of full factorial experiment (FFE). Due to this localization, the equality of covariance matrices of the classes is ensured and the share of the correctly classified objects increases. We proposed, to overcoming the main shortcoming of the given research, connected to the absence of functional dependencies, which make it possible to quantitatively evaluate the accuracy of classification at various variants of arrangement of FFE plans, to use the methods of experimental optimization. Its purpose is the selection of such coordinates of the centers of plans and their geometric characteristics for classes A and B, which ensure the maximum share of the correctly classified objects based on the obtained decision rules. The realization of this approach may become the reserve for the increase in the accuracy of classification with application of the methods of parametric optimization.

Author Biography

Mourad Aouati, Central City Police Department of Constantine Ali Mendjeli UV 01 Ilot 03 Bt H n°123 Constantine Algeria

Chief Commissioner of Police

References

  1. Nemrodov, D., Niemeier, M., Mok, J. N. Y., Nestor, A. (2016). The time course of individual face recognition: A pattern analysis of ERP signals. NeuroImage, 132, 469–476. doi: 10.1016/j.neuroimage.2016.03.006
  2. Liu, L., Fieguth, P., Zhao, G., Pietikäinen, M., Hu, D. (2016). Extended local binary patterns for face recognition. Information Sciences, 358-359, 56–72. doi: 10.1016/j.ins.2016.04.021
  3. Florindo, J. B., Bruno, O. M. (2016). Local fractal dimension and binary patterns in texture recognition. Pattern Recognition Letters, 78, 22–27. doi: 10.1016/j.patrec.2016.03.025
  4. Barajas-García, C., Solorza-Calderón, S., Álvarez-Borrego, J. (2016). Classification of fragments of objects by the Fourier masks pattern recognition system. Optics Communications, 367, 335–345. doi: 10.1016/j.optcom.2016.01.059
  5. Kushnirenko, O. A., Cadchenko, A. V., Troyanskiy, A. V. (2013). Correlation algorithm of recognition of character information with low-complexity of technical implementation. Eastern-European Journal of Enterprise Technologies, 3 (9 (63)), 42–45. Available at: http://journals.uran.ua/eejet/article/view/14848/12650
  6. Kasian, K., Bratchykov, V., Shkarupylo, V. (2015). Development of modified method for text recognition in standardized picture. Eastern-European Journal of Enterprise Technologies, 3 (2 (75)), 11–17. doi: 10.15587/1729-4061.2015.43047
  7. Fraze-Frazenko, A. (2012). Method for the regularization of ill-posed problems of recognition in CCTV. Technology audit and production reserves, 6 (4 (8)), 19–20. Available at: http://journals.uran.ua/tarp/article/view/5638/5080
  8. Anokhin, M. (2014). Moving objects recognition method by their video images. Eastern-European Journal of Enterprise Technologies, 4 (9 (70)), 33–37. doi: 10.15587/1729-4061.2014.26275
  9. Voloshyn, N. (2012). Identification of iris sign in automated systems of iridology based on Petri Nets. Technology audit and production reserves, 4 (1 (6)), 5–6. Available at: http://journals.uran.ua/tarp/article/view/4767/4418
  10. Alfaro-Ponce, M., Argüelles, A., Chairez, I. (2016). Pattern recognition for electroencephalographic signals based on continuous neural networks. Neural Networks, 79, 88–96. doi: 10.1016/j.neunet.2016.03.004
  11. Nesterenko, S. A., Stanovskiy, A. O., Oborotova, O. O. (2015). The wireless computer networks state recognition over the three-dimensional field of directions. Technology audit and production reserves, 6 (2 (26)), 28–35. doi: 10.15587/2312-8372.2015.56825
  12. Unglert, K., Radić, V., Jellinek, A. M. (2016). Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra. Journal of Volcanology and Geothermal Research, 320, 58–74. doi: 10.1016/j.jvolgeores.2016.04.014
  13. Stuper, A. J., Brugger, W. E., Jurs, P. C. (1979). Computer Assisted Studies of Chemical Structure and Biological Function. Published by John Wiley & Sons.
  14. Hartmann, K., Lezki, E., Schafer, V. (1974). Statistische Versuchsplanung und auswertung in der Stoffwirtschaft. Leipzig.
  15. Pavlenko, V. D., Fomin, A. A. (2001). Povyishenie tochnosti postroeniya reshayuschego pravila v metodah statisticheskoy klassifikatsii. Elektronnoe modelirovanie, 23 (4), 61–68.
  16. Vasenko, Y. (2012). Technology for improved wear iron. Technology audit and production reserves, 1 (1 (3)), 17–21. Available at: http://journals.uran.ua/tarp/article/view/4870/4521
  17. Ponomarenko, O. I., Trenev, N. S. (2013). Computer modeling of crystallization processes as a reserve of improving the quality of pistons of ICE. Technology audit and production reserves, 6 (2 (14)), 36–40. Available at: http://journals.uran.ua/tarp/article/view/19529/17205
  18. Ignaszaka, Z., Popielarskia, P., Krawiecb, K. (2007). Contribute to quantitative identification of casting defects based on computer analysis of X-ray images. Archives of foundry engineering, 7 (4 (18/4)), 89–94.
  19. Lin, Z., Lyu, M. R., King, I. (2011). MatchSim: a novel similarity measure based on maximum neighborhood matching. Knowledge and Information Systems, 32 (1), 141–166. doi: 10.1007/s10115-011-0427-z
  20. Arsirii, Е., Manikaeva, О., Vasilevskaja, O. (2015). Development of the decision support subsystem in the systems of neural network pattern recognition by statistical information. Eastern-European Journal of Enterprise Technologies, 6 (4 (78)), 4–12. doi: 10.15587/1729-4061.2015.56429
  21. Fraze-Frazenko, A. (2012). Algorithm of study neural network for image recognition. Technology audit and production reserves, 4 (1 (6)), 33–34. Available at: http://journals.uran.ua/tarp/article/view/4781/4432
  22. Fertsev, A. A. (2012). Uskorenie obucheniya neyronnoy seti dlya raspoznavaniya izobrazheniy s pomoschyu tehnologii NVidia Cuda. Vestnik Samarskogo gosudarstvennogo tehnicheskogo universiteta, 1 (26), 183–191.
  23. Unglert, K., Radić, V., Jellinek, A. M. (2016). Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra. Journal of Volcanology and Geothermal Research, 320, 58–74. doi: 10.1016/j.jvolgeores.2016.04.014
  24. Fakhar, K., El Aroussi, M., Saidi, M. N., Aboutajdine, D. (2016). Fuzzy pattern recognition-based approach to biometric score fusion problem. Fuzzy Sets and Systems. doi: 10.1016/j.fss.2016.05.005
  25. Perova, I. G. (2014). Adaptive treatment of these mediсo-biological researches by methods of computational intelligence. Eastern-European Journal of Enterprise Technologies, 1 (4 (67)), 24–28. doi: 10.15587/1729-4061.2014.21202
  26. Demin, D. A., Pelikh, V. F., Ponomarenko, O. I. (1998). Complex alloying of grey cast iron. Litejnoe Proizvodstvo, 10, 18–19.
  27. Demin, D. A. (1998). Change in cast iron's chemical composition in inoculation with a Si-V-Mn master alloy. Litejnoe Proizvodstvo, 6, 35.

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Published

2016-08-30

How to Cite

Aouati, M. (2016). Localization of vectors–patterns in the problems of parametric classification with the purpose of increasing its accuracy. Eastern-European Journal of Enterprise Technologies, 4(4(82), 10–20. https://doi.org/10.15587/1729-4061.2016.76171

Issue

Vol. 4 No. 4(82) (2016): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2016 Aouati Mourad

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