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A New RBFNDDA-KNN Network and Its Application to Medical Pattern Classification

  • Shing Chiang Tan
  • Chee Peng Lim
  • Robert F. Harrison
  • R. Lee Kennedy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)

Abstract

In this paper, a new variant of the Radial Basis Function Network with the Dynamic Decay Adjustment algorithm (i.e., RBFNDDA) is introduced for undertaking pattern classification problems with noisy data. The RBFNDDA network is integrated with the k-nearest neighbours algorithm to form the proposed RBFNDDA-KNN model. Given a set of labelled data samples, the RBFNDDA network undergoes a constructive learning algorithm that exhibits a greedy insertion behaviour. As a result, many prototypes (hidden neurons) that represent small (with respect to a threshold) clusters of labelled data are introduced in the hidden layer. This results in a large network size. Such small prototypes can be caused by noisy data, or they can be valid representatives of small clusters of labelled data. The KNN algorithm is used to identify small prototypes that exist in the vicinity (with respect to a distance metric) of the majority of large prototypes from different classes. These small prototypes are treated as noise, and are, therefore, pruned from the network. To evaluate the effectiveness of RBFNDDA-KNN, a series of experiments using pattern classification problems in the medical domain is conducted. Benchmark and real medical data sets are experimented, and the results are compared, analysed, and discussed. The outcomes show that RBFNDDA-KNN is able to learn information with a compact network structure and to produce fast and accurate classification results.

Keywords

Radial basis function neural network Nearest neighbour Pattern classification 

References

  1. 1.
    Lee, J., Steele, C.M., Chau, T.: Classification of healthy and abnormal swallows based on accelerometry and nasal airflow signals. AI Med. 52, 17–25 (2011)Google Scholar
  2. 2.
    Yu, J.-b., Xi., L.-f.: A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. Expert Syst. Appl. 36, 909–921 (2009)Google Scholar
  3. 3.
    Barakat, M., Druaux, F., Lefebvre, D., Khalil, M., Mustapha, O.: Self adaptive growing neural network classifier for faults detection and diagnosis. Neurocomputing 74, 3865–3876 (2011)CrossRefGoogle Scholar
  4. 4.
    Son, C.-S., Kim, Y.-N., Kim, H.-S., Park, H.-S., Kim, M.-S.: Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches. J. Biomed. Inform. 45, 999–1008 (2012)CrossRefGoogle Scholar
  5. 5.
    Ciflikli, C., Kahya-Özyirmidokuz, E.: Implementing a data mining solution for enhancing carpet manufacturing productivity. Knowl. Based Syst. 23, 783–788 (2010)CrossRefGoogle Scholar
  6. 6.
    Upendar, J., Gupta, C.P., Singh, G.K.: Statistical decision-tree based fault classification scheme for protection of power transmission lines. Electr. Power Energy Syst. 36, 1–12 (2012)CrossRefGoogle Scholar
  7. 7.
    Alayón, S., Robertson, R., Warfield, S.K., Ruiz-Alzola, J.: A fuzzy system for helping medical diagnosis of malformations of cortical development. J. Biomed. Inform. 40, 221–235 (2007)CrossRefGoogle Scholar
  8. 8.
    Piltan, M., Mehmanchi, E., Ghaderi, S.F.: Proposing a decision-making model using analytical hierarchy process and fuzzy expert system for prioritizing industries in installation of combined heat and power systems. Expert Syst. Appl. 39, 1124–1133 (2012)CrossRefGoogle Scholar
  9. 9.
    Kazemi, M.V., Moradi, M., Kazemi, R.V.: Minimization of powers ripple of direct power controlled DFIG by fuzzy controller and improved discrete space vector modulation. Expert Syst. Appl. 89, 23–30 (2012)Google Scholar
  10. 10.
    Paetz, J.: Reducing the number of neurons in radial basis function networks with dynamic decay adjustment. Neurocomputing 62, 79–91 (2004)CrossRefGoogle Scholar
  11. 11.
    Davey, P., Lalloo, D.G.: Drug induced chest pain—rare but important. Postgrad Med J. 76, 420–422 (2000)CrossRefGoogle Scholar
  12. 12.
    Berthold, M.R., Diamond, J.: Constructive training of probabilistic neural networks. Neurocomputing 19, 167–183 (1998)CrossRefGoogle Scholar
  13. 13.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967)CrossRefMATHGoogle Scholar
  14. 14.
    Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques. A generalized knearest neighbor rule, pp. 64–84. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
  15. 15.
    Asuncion, A., Newman, D.J.: UCI machine learning repository. University of California, School of Information and Computer Science, Irvine. [http://www.ics.uci.edu/\(\sim \)mlearn/MLRepository.html] (2007)Google Scholar
  16. 16.
    Specht, D.F.: Probabilistic neural networks. Neural Netw. 3, 109–118 (1990)CrossRefGoogle Scholar
  17. 17.
    Hudak, M.H.: RCE Classifiers: theory and practice. Cybern. Syst. 23, 483–515 (1992)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Reilly, D.L., Cooper, L.N., Elbaum, C.: A neural model for category learning. Biol. Cybern. 45, 35–41 (1982)CrossRefGoogle Scholar
  19. 19.
    Wang, S.-J., Mathew, A., Chen, Y., Xi, L.-F., Ma, L., Lee, J.: Empirical analysis of support vector machine ensemble classifiers. Expert Syst. Appl. 36, 6466–6476 (2009)CrossRefGoogle Scholar
  20. 20.
    Lim, C.P., Kuan, M.M., Harrison, R.F.: Application of fuzzy ARTMAP and fuzzy c—means clustering to pattern classification with incomplete data. Neural Comput. Appl. 14, 104–113 (2005)CrossRefGoogle Scholar
  21. 21.
    Efron, B.: Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26 (1979)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Lam, W., Keung, C.-K., Liu, D.: Discovering useful concept prototypes for classification based on filtering and abstraction. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1075–1090 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shing Chiang Tan
    • 1
  • Chee Peng Lim
    • 2
  • Robert F. Harrison
    • 3
  • R. Lee Kennedy
    • 4
  1. 1.Faculty of Information Science and TechnologyMultimedia UniversityMelakaMalaysia
  2. 2.Centre for Intelligent Systems ResearchDeakin UniversityBurwoodAustralia
  3. 3.Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
  4. 4.School of MedicineDeakin UniversityBurwoodAustralia

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