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Probabilistic Neural Network Structure Reduction for Medical Data Classification

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Book cover Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

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Abstract

Probabilistic neural network (PNN) consists of the number of pattern neurons that equals the cardinality of the data set. The model design is therefore complex for large database classification problems. In this article, two effective PNN reduction procedures are introduced. In the first approach, the PNN’s pattern layer neurons are reduced by means of a k-means clustering procedure. The second method uses a support vector machines algorithm to select pattern layer nodes. Modified PNN networks are compared with the original model in medical data classification problems. The prediction ability expressed in terms of the 20% test set error for the networks is assessed. By means of the experiments, it is shown that the appropriate pruning of the pattern layer neurons in the PNN enhances the performance of the classifier.

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References

  1. Specht, D.F.: Probabilistic Neural Networks. Neural Networks 3, 109–118 (1990)

    Article  Google Scholar 

  2. Specht, D.F.: Enhancements to the probabilistic neural networks. In: IEEE International Joint Conference on Neural Networks, pp. 761–768. IEEE Press, Baltimore (1992)

    Google Scholar 

  3. Chtioui, Y., Bertrand, D., Barba, D.: Reduction of the size of the learning data in a probabilistic neural network by hierarchical clustering. Application to the discrimination of seeds by artificial vision. Chemometrics and Intelligent Laboratory Systems 35, 175–186 (1996)

    Article  Google Scholar 

  4. Franti, P., Kaukoranta, T., Shen, D.-F., Chang, K.-S.: Fast and Memory Efficient Implementation of the Exact PNN. IEEE Trans. Image Processing 9, 773–777 (2000)

    Article  Google Scholar 

  5. Adeli, H., Panakkat, A.: A probabilistic neural network for earthquake magnitude prediction. Neural Networks 22, 1018–1024 (2009)

    Article  Google Scholar 

  6. Gorunescu, F., Gorunescu, M., El-Darzi, E., Gorunescu, S.: An evolutionary computational approach to probabilistic neural network with application to hepatic cancer diagnosis. In: IEEE Symposium on Computer-Based Medical Systems, pp. 461–466. IEEE Press, Dublin (2005)

    Chapter  Google Scholar 

  7. Shan, Y., Zhao, R., Xu, G., Liebich, H.M., Zhang, Y.: Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data. Analytica Chimica Acta 471, 77–86 (2002)

    Article  Google Scholar 

  8. Huang, C.-J., Liao, W.-C.: A Comparative Study of Feature Selection Methods for Probabilistic Neural Networks in Cancer Classification. In: IEEE International Conference on Tools with Artificial Intelligence, pp. 451–458. IEEE Press, Sacramento (2003)

    Chapter  Google Scholar 

  9. Mantzaris, D., Anastassopoulos, G., Adamopoulos, A.: Genetic algorithm pruning of probabilistic neural networks in medical disease estimation. Neural Networks 24, 831–835 (2011)

    Article  Google Scholar 

  10. Rutkowski, L.: Adaptive Probabilistic Neural Networks for Pattern Classification in Time-Varying Environment. IEEE Trans. Neural Networks 15, 811–827 (2004)

    Article  Google Scholar 

  11. Traven, H.G.C.: A Neural Network Approach to Statistical Pattern Classification by ’Semiparametic’ Estimation of Probability Density Functions. IEEE Trans. Neural Networks 2, 366–377 (1991)

    Article  Google Scholar 

  12. Burrascano, P.: Learning Vector Quantization for the Probabilistic Neural Network. IEEE Trans. Neural Networks 2, 458–461 (1991)

    Article  Google Scholar 

  13. Streit, R.L., Luginbuhl, T.E.: Maximum Likelihood Training of Probabilistic Neural Networks. IEEE Trans. Neural Networks 5, 764–783 (1994)

    Article  Google Scholar 

  14. Zaknich, A.: A vector quantisation reduction method for the probabilistic neural network. In: IEEE International Conference on Neural Networks, pp. 1117–1120. IEEE Press, Huston (1997)

    Google Scholar 

  15. Berthold, M.R., Diamond, J.: Constructive training of probabilistic neural networks. Neurocomputing 19, 167–183 (1998)

    Article  Google Scholar 

  16. Mao, K.Z., Tan, K.-C., Ser, W.: Probabilistic Neural-Network Structure Determination for Pattern Classification. IEEE Trans. Neural Networks 11, 1009–1016 (2000)

    Article  Google Scholar 

  17. Specht, D.F., Romsdahl, H.: Experience with adaptive probabilistic neural networks and adaptive general regression neural networks. In: IEEE World Congress on Computational Intelligence 2, pp. 1203–1208. IEEE Press, Orlando (1994)

    Google Scholar 

  18. Parzen, E.: On estimation of a probability density function and mode. Annals of Mathematical Statistics 36, 1065–1076 (1962)

    Article  MathSciNet  Google Scholar 

  19. Masters, T.: Practical Neural Networks Recipes in C++. Academic Press, San Diego (1993)

    Google Scholar 

  20. Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. J. Royal Stat. Soci.–Ser. C (Applied Statistics) 1, 100–108 (1979)

    Google Scholar 

  21. Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowledge Information Systems 14, 1–37 (2008)

    Article  Google Scholar 

  22. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    Book  MATH  Google Scholar 

  23. Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Computation 12, 1207–1245 (2000)

    Article  Google Scholar 

  24. Gunn, S.R.: Support Vector Machines for Classification and Regression. Technical Report, University of Southampton (1998)

    Google Scholar 

  25. Schölkopf, B., Burges, C.J.C., Smola, A.J.: Advances in Kernel Methods – Support Vector Learning. MIT Press, Cambridge (1999)

    Google Scholar 

  26. Scholköpf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)

    Google Scholar 

  27. UCI Machine Learning Repository, archive.ics.uci.edu/ml/datasets.html

  28. Skret, A., Lozinski, T., Chrusciel, A.: Epidemiology of ovarian cancer: Hormonal and genetic aspects. In: Congress of the European Society for Gynecologic and Obstetric Investigation, pp. 189–205. CIC Edizioni Internazionali, Rome (2001)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959, Rzeszow, W. Pola 2, Poland

    Maciej Kusy & Jacek Kluska

Authors
  1. Maciej Kusy
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  2. Jacek Kluska
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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Kusy, M., Kluska, J. (2013). Probabilistic Neural Network Structure Reduction for Medical Data Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_11

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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