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MIMO Pseudo Neural Networks for Iris Data Classification

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Modern Trends and Techniques in Computer Science

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 285))


This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure which serves as a classifier. Compared to previous research, this paper synthesizes multi-input–multi-output (MIMO) classifiers. Classical artificial neural networks (ANN) were an inspiration for this work. The proposed technique creates a relation between inputs and outputs as a whole structure together with numerical values which could be observed as weights in ANN. The Analytic Programming (AP) was utilized as the tool of synthesis by means of the evolutionary symbolic regression. Iris data (a known benchmark for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used.

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  1. Gurney, K.: An Introduction to Neural Networks. CRC Press, Boca Raton (1997). ISBN 1857285034

    Google Scholar 

  2. Hertz, J., Kogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Addison-Wesley, Reading (1991)

    Google Scholar 

  3. Wasserman, P.D.: Neural Computing: Theory and Practice. Coriolis Group (1980). ISBN 0442207433

    Google Scholar 

  4. Fausett, L.V.: Fundamentals of Neural Networks: Architectures, Algorithms and Applications. Prentice Hall, Englewood Cliffs (1993). ISBN 9780133341867

    Google Scholar 

  5. Fekiac, J., Zelinka, I., Burguillo, J.C.: A review of methods for encoding neural network topologies in evolutionary computation. In: ECMS 2011, Krakow, Poland. ISBN 978-0-9564944-3-6

    Google Scholar 

  6. Back, T., Fogel, D.B., Michalewicz, Z.: Handbook of evolutionary algorithms. Oxford University Press, Oxford (1997). ISBN 0750303921

    Google Scholar 

  7. Koza, J.R., et al.: Genetic Programming III; Darwinian Invention and Problem Solving. Morgan Kaufmann Publisher, Los Altos (1999). ISBN 1-55860-543-6

    Google Scholar 

  8. Koza, J.R.: Genetic Programming. MIT Press, Cambridge (1998). ISBN 0-262-11189-6

    Google Scholar 

  9. O’Neill, M., Ryan, C.: Grammatical Evolution. Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Dordrecht (2003). ISBN 1402074441

    Google Scholar 

  10. Zelinka, I., et al.: Analytical programming—A novel approach for evolutionary synthesis of symbolic structures. In: Kita, E. (ed.) Evolutionary Algorithms, InTech (2011). ISBN 978-953-307-171-8

    Google Scholar 

  11. Oplatkova, Z.: Metaevolution: Synthesis of Optimization Algorithms by means of Symbolic Regression and Evolutionary Algorithms. Lambert Academic Publishing, Saarbrücken (2009). ISBN 978-3-8383-1808-0

    Google Scholar 

  12. Zelinka, I., Varacha, P., Oplatkova, Z.: Evolutionary synthesis of neural network. In: 12th International Conference on Softcomputing (Mendel 2006), Brno, Czech Republic, 31 May–2 June 2006, pp. 25–31. ISBN 80-214-3195-4

    Google Scholar 

  13. Zelinka, I., Oplatkova, Z., Nolle, L.: Boolean symmetry function synthesis by means of arbitrary evolutionary algorithms-comparative study. Int. J. Simul. Syst. Sci. Technol. 6(9), 44–56 (2005). ISSN 1473-8031

    Google Scholar 

  14. Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7(2), 179–188 (1936). doi:10.1111/j.1469-1809.1936.tb02137.x

    Article  Google Scholar 

  15. Machine learning repository with Iris data set,

  16. Swain, M., et al.: An approach for iris plant classification using neural network. Int. J. Soft Comput. 3(1) (2012). doi:10.5121/ijsc.2012.3107

  17. Shekhawat, P., Dhande, S.S.: Building and iris plant data classifier using neural network associative classification. Int. J. Adv. Technol. 2(4), 491–506 (2011). ISSN 0976-4860

    Google Scholar 

  18. Avci, M., Yildirim, T.: Microcontroller based neural network realization and iris plant classifier application. In: Proceedings of the Twelfth Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN’03), Canakkale, Turkey, 2–4 July 2003

    Google Scholar 

  19. Osselaer, S.: Iris data analysis using back propagation neural networks. J. Manuf. Syst. 13(4), 262 (2003)

    Google Scholar 

  20. Chen, S., Fang, Y.: A new approach for handling iris data classification problem. Int. J. Appl. Sci. Eng. (2005). ISSN 1727-2394

    Google Scholar 

  21. Kostin, A.: A simple and fast multi-class piecewise linear pattern classifier. Pattern Recogn. 39(11), 1949–1962 (2006). ISSN 0031-3203. doi:10.1016/j.patcog.2006.04.022

  22. Kim, D.: Data classification based on tolerant rough set. Pattern Recogn. 34(8), 1613–1624 (2001). ISSN 0031-3203. doi:10.1016/S0031-3203(00)00057-1

  23. Agustín-Blas, L.E., et al.: A new grouping genetic algorithm for clustering problems. Expert Syst. Appl. 39(10), 9695–9703 (2002). ISSN 0957-4174. doi:10.1016/j.eswa.2012.02.149

  24. Zhou, E., Khotanzad, A.: Fuzzy classifier design using genetic algorithms. Pattern Recogn. 40(12), 3401–3414 (2007). ISSN 0031-3203. doi:10.1016/j.patcog.2007.03.028

  25. Ferreira, C.: Gene Expression Programming: Mathematical Modelling by an Artificial Intelligence (2006). ISBN 9729589054

    Google Scholar 

  26. Lampinen, J., Zelinka, I.: New Ideas in Optimization—Mechanical Engineering Design Optimization by Differential Evolution, vol. 1, 20 p. McGraw-hill, London (1999). ISBN 007-709506-5

    Google Scholar 

  27. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization, 1st edn. Natural Computing Series. Springer, Berlin (2005)

    Google Scholar 

  28. Volna, E., Kotyrba, M., Jarusek, R.: Multiclassifier based on Elliott wave’s recognition. Comput. Math. Appl. 66 (2013). ISSN 0898-1221. doi:10.1016/j.camwa.2013.01.012

  29. Oplatkova, Z., Senkerik, R.: Evolutionary synthesis of complex structures—Pseudo neural networks for the task of iris dataset classification. In: Zelinka, I., Chen, G., Rössler, O.E., Snasel, V., Abraham, A. (eds.) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems, vol. 210, pp. 211–220. Advances in Intelligent Systems and Computing. Springer International Publishing, Switzerland (2013). doi:10.1007/978-3-319-00542-3_22

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This work was supported by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089.

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Correspondence to Zuzana Kominkova Oplatkova .

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Oplatkova, Z.K., Senkerik, R. (2014). MIMO Pseudo Neural Networks for Iris Data Classification. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Modern Trends and Techniques in Computer Science. Advances in Intelligent Systems and Computing, vol 285. Springer, Cham.

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  • Print ISBN: 978-3-319-06739-1

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