Skip to main content
SpringerLink
Log in

A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm

  • Original Paper
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper presents a novel conflict-resolving neural network classifier that combines the ordering algorithm, fuzzy ARTMAP (FAM), and the dynamic decay adjustment (DDA) algorithm, into a unified framework. The hybrid classifier, known as Ordered FAMDDA, applies the DDA algorithm to overcome the limitations of FAM and ordered FAM in achieving a good generalization/performance. Prior to network learning, the ordering algorithm is first used to identify a fixed order of training patterns. The main aim is to reduce and/or avoid the formation of overlapping prototypes of different classes in FAM during learning. However, the effectiveness of the ordering algorithm in resolving overlapping prototypes of different classes is compromised when dealing with complex datasets. Ordered FAMDDA not only is able to determine a fixed order of training patterns for yielding good generalization, but also is able to reduce/resolve overlapping regions of different classes in the feature space for minimizing misclassification during the network learning phase. To illustrate the effectiveness of Ordered FAMDDA, a total of ten benchmark datasets are experimented. The results are analyzed and compared with those from FAM and Ordered FAM. The outcomes demonstrate that Ordered FAMDDA, in general, outperforms FAM and Ordered FAM in tackling pattern classification problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Andonie R and Sasu L (2006). Fuzzy ARTMAP with input relevances. IEEE Trans Neural Netw 17: 929–941

    Article  Google Scholar 

  2. Carpenter GA and Ross W (1995). ART-EMAP: a neural network architecture for learning and prediction by evidence accumulation. IEEE Trans Neural Netw 6: 805–818

    Article  Google Scholar 

  3. Carpenter GA, Grossberg S, Markuzon N, Reynolds J and Rosen D (1992). Fuzzy ARTMAP: a neural network architecture for incremental learning of analog multidimensional maps. IEEE Trans Neural Netw 3: 698–713

    Article  Google Scholar 

  4. Carpenter GA, Milenova B and Noeske B (1998). Distributed ARTMAP: a neural network for fast distributed supervised learning. Neural Netw 11: 793–813

    Article  Google Scholar 

  5. Dagher I, Georgiopoulos M, Heileman G, Bebis G (1998) Fuzzy ARTVar: an improved fuzzy ARTMAP algorithm. In: Proceedings of IEEE world congress computational intelligence WCCI’98, pp 1688–1693

  6. Dagher I, Georgiopoulos M, Heileman GL and Bebis G (1999). An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance. IEEE Trans Neural Netw 10: 768–778

    Article  Google Scholar 

  7. Efron B (1979). Bootstrap methods: another look at the jackknife. Ann Stat 7: 1–26

    Article  MATH  MathSciNet  Google Scholar 

  8. Gomez-Sanchez E, Dimitriadis Y, Cano-Izquierdo J and Lopez-Coronado J (2002). ARTMAP: use of mutual information for category reduction in fuzzy ARTMAP. IEEE Trans Neural Netw 13: 58–69

    Article  Google Scholar 

  9. Guo GD and Li SZ (2003). Content-based audio classification and retrieval by support vector machines. IEEE Trans Neural Netw 14: 209–214

    Article  Google Scholar 

  10. Hettich S, Blake CL, Merz CJ (1998) UCI repository of machine learning databases [http://www.ics.uci.edu/∼mlearn/MLRepository.html]. Department of Information and Computer Science, University of California, Irvine, CA

  11. Ishibuchi H, Yamamoto T and Nakashima T (2005). Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Trans Syst Man Cybern B Cybern 35: 359–365

    Article  Google Scholar 

  12. Justino EJR, Bortolozzi F and Sabourin R (2005). A comparison of SVM and HMM classifiers in the off-line signature verification. Pattern Recognit Lett 26: 1377–1385

    Article  Google Scholar 

  13. Lim CP and Harrison RF (1997). An incremental adaptive network for on-line supervised learning and probability estimation. Neural Netw 10: 925–939

    Article  Google Scholar 

  14. Marriott S and Harrison RF (1995). A modified fuzzy ARTMAP architecture for the approximation of noisy mappings. Neural Netw 8: 619–641

    Article  Google Scholar 

  15. Moody MJ and Darken CJ (1989). Fast learning in networks of locally-tuned processing units. Neural Comput 1: 281–294

    Article  Google Scholar 

  16. Pernkopfa F (2005). Bayesian network classifiers versus selective k-NN classifier. Pattern Recognit 38: 1–10

    Article  Google Scholar 

  17. Polikar R, Udpa L, Udpa SS and Honovar V (2001). Learn: an incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cybern C 31: 497–508

    Article  Google Scholar 

  18. Reilly DL, Cooper LN and Elbaum C (1982). A neural model for category learning. Biol Cybern 45: 35–41

    Article  Google Scholar 

  19. Ripley BD (1994) Neural networks and related methods for classification. J R Stat Soc B 56:409–456

    MATH  MathSciNet  Google Scholar 

  20. Roy A (2000). Artificial neural networks—a science in trouble. ACM SIGKDD Explor 1: 33–38

    Article  Google Scholar 

  21. Simpson PK (1992). Fuzzy min–max neural networks—part 1: classification. IEEE Trans Neural Netw 3: 776–786

    Article  Google Scholar 

  22. Verzi S, Heileman G, Georgiopoulos M, Healy M (1998) Boosted ARTMAP. In: Proceedings of IEEE world congress computational intelligence WCCI’98, pp 396–400

  23. Vigdor B and Lerner B (2006). Accurate and fast off and online fuzzy ARTMAP-based image classification with application to genetic abnormality diagnosis. IEEE Trans Neural Netw 17: 1288–1300

    Article  Google Scholar 

  24. Williamson J (1996). Gaussian ARTMAP: a neural network for fast incremental learning of noisy multidimensional maps. Neural Netw 9: 881–897

    Article  Google Scholar 

  25. Wu Y, Ianakiev K and Govindaraju V (2002). Improved k-nearest neighbor classification. Pattern Recognit 35: 2311–2318

    Article  MATH  Google Scholar 

  26. Zhang GP (2000). Neural networks for classification: a survey. IEEE Trans Syst Man Cybern C Appl Rev 30: 451–462

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450, Melaka, Malaysia

    Shing Chiang Tan

  2. Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450, Melaka, Malaysia

    M. V. C. Rao

  3. School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia

    Chee Peng Lim

Authors
  1. Shing Chiang Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. V. C. Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chee Peng Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shing Chiang Tan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tan, S.C., Rao, M.V.C. & Lim, C.P. A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm. Soft Comput 12, 765–775 (2008). https://doi.org/10.1007/s00500-007-0235-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-007-0235-2

Keywords

Search

Navigation