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Cost Functions Based on Different Types of Distance Measurements for Pseudo Neural Network Synthesis

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Mendel 2015 (ICSC-MENDEL 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 378))

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Abstract

This research deals with a novel approach to classification. New classifiers are synthesized as a complex structure via evolutionary symbolic computation techniques. Compared to previous research, this paper synthesizes multi-input-multi-output (MIMO) classifiers with different cost function based on distance measurements. An inspiration for this work came from the field of artificial neural networks (ANN). 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. Distances used in cost functions were: Manhattan (absolute distances of output vectors), Euclidean, Chebyshev (maximum distance value), Canberra distance, Bray – Curtis. The Analytic Programming (AP) was utilized as the tool of synthesis by means of the evolutionary symbolic regression. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used Iris data (a known benchmark for classifiers) was used for testing of the proposed method.

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References

  1. Zelinka, et al.: Analytical programming - a novel approach for evolutionary synthesis of symbolic structures. In: Kita, E. (ed.) Evolutionary Algorithms, InTech (2011)

    Google Scholar 

  2. Oplatkova, Z.: Metaevolution: Synthesis of Optimization Algorithms by means of Symbolic Regression and Evolutionary Algorithms, Lambert Academic Publishing Saarbrücken (2009)

    Google Scholar 

  3. Zelinka, I., Varacha, P., Oplatkova, Z.: Evolutionary synthesis of neural network. In: Proceedings of 12th International Conference on Soft Computing – MENDEL 2006. Mendel series, vol. 2006, pp. 25 – 31. Brno, Czech Republic (2006). ISSN: 1803- 3814

    Google Scholar 

  4. Zelinka, I.,Oplatkova, Z., Nolle, L.: Boolean symmetry function synthesis by means of arbitrary evolutionary algorithms-comparative study. In: International Journal of Simulation Systems, Science and Technology, vol. 6, pp. 44–56, 9 Aug 2005. ISSN: 1473-8031

    Google Scholar 

  5. 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 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

  11. Fekiac, J., Zelinka, I., Burguillo, J.C.: A Review of Methods for Encoding Neural Network Topologies in Evolutionary Computation. ECMS 2011, Krakow, Poland. ISBN: 978-0-9564944-3-6

    Google Scholar 

  12. Back, T., Fogel, D.B., Michalewicz, Z.: Handbook of Evolutionary Algorithms. Oxford University Press (1997). ISBN 0750303921

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  16. 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 

  17. Machine learning repository with Iris data set. http://archive.ics.uci.edu/ml/datasets/Iris

  18. 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

  19. 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 

  20. 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’2003), Canakkale, Turkey, 2–4 July 2003

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

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

  25. Agustı´n-Blas, L.E., et al.: A new grouping genetic algorithm for clustering problems. Expert Syst. Appl. 39(10) 9695–9703 (2012). doi:10.1016/j.eswa.2012.02.149. ISSN 0957-4174

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

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

    Google Scholar 

  28. 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. Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 211–220. doi:10.1007/978-3-319-00542-3_22

  29. Kominkova Oplatkova, Z., Senkerik, R.: Lenses classification by means of pseudo neural networks – two approaches. In: MENDEL 2014 - 20th International conference on soft Computing. Brno, Czech Republic, pp. 397–401. ISSN 1803-3814. (2014)

    Google Scholar 

  30. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution : A Practical Approach to Global Optimization. (Natural Computing Series), Springer; 1 edition (2005)

    Google Scholar 

  31. Rohlf, F.J.: Brenner’s Encyclopedia of Genetics (Second Edition) (2013)

    Google Scholar 

  32. Matousek, R., Karpisek, Z.: Exotic metrics for function approximation. In: Proceedings of 17th International Conference on Soft Computing – MENDEL 2011. Mendel series vol. 2011, pp. 560–566, Brno (2011), ISSN: 1803- 3814

    Google Scholar 

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Acknowledgment

This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by financial support of research project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also 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. (2015). Cost Functions Based on Different Types of Distance Measurements for Pseudo Neural Network Synthesis. In: Matoušek, R. (eds) Mendel 2015. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-319-19824-8_24

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  • DOI: https://doi.org/10.1007/978-3-319-19824-8_24

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