SpringerLink
Log in

Structure adaptation of hierarchical knowledge-based classifiers

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper introduces a new method to identify the qualified rule-relevant nodes to construct hierarchical neuro-fuzzy systems (HNFSs). After learning, the proposed method analyzes the entire history of activities and behaviors of all rule nodes, which reflects their levels of involvement or contribution during the process. The less qualified rule-relevant nodes can then be identified and removed, reducing the size and complexity of the HNFS. Upon the repetitive learning process, the method may be repetitively applied until a satisfactory result is obtained, simultaneously improving the performance and reducing the size and complexity. Incorporated with the method is a new HNFS architecture which addresses both the scalability problem experienced in rule based systems and the restriction of the “overcrowded defuzzification” problem found in hierarchical designs. In order to verify the performance, the proposed method has been successfully tested against five well-known classification problems whose results are provided and then discussed in the concluding remarks.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Nauck DD, Klawonn F, Kruse R (1997) Foundations of neuro-fuzzy systems. Wiley, Chichester

    Google Scholar 

  2. Nauck DD, Kruse R (1997) A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets Syst 89(3):277–288

    Article  MathSciNet  Google Scholar 

  3. Nauck DD, Kruse R (1999) Neuro-fuzzy systems for function approximaton. Fuzzy Sets Syst 101(2):205–313

    Article  MathSciNet  Google Scholar 

  4. Nauck DD (2003) Fuzzy data analysis with NEFCLASS. Int J Approximate Reasoning 32(2–3):103–130

    Article  MATH  MathSciNet  Google Scholar 

  5. Jang JSR, Sun CT (1993) Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans Neural Netw 4(1):156–159

    Article  Google Scholar 

  6. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  MathSciNet  Google Scholar 

  7. Halgamuge SK, Glesner M (1994) Neural networks in designing fuzzy systems for real world applications. Fuzzy Sets Syst 65:1–12

    Article  Google Scholar 

  8. Halgamuge SK (1994) FuNeGen 1.0. http://www.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ai/areas/fuzzy/systems/funegen/

  9. Gorzalczany MB, Gradzki P (1999) Computational intelligence in medical decision support-a comparison of two neuro-fuzzy systems. In: Proceedings of IEEE international symposium on Ind electronics 1999, pp 408–413

  10. Halgamuge SK, Hermann CS, Jain L (1996) Analysis of EEG signals with wavelets and knowledge engineering techniques. In: Amari S, Xu L, Chan LW, King I, Leung KS (eds) Progress in Neural Information Processing, vol 2. Springer, Heidelberg, pp 1381–1386

  11. Chang BCH, Halgamuge SK (2000) Model free on-line adaptive feedback control with FuNe I AFC neuro-fuzzy system. In: Proceedings of the ninth IEEE international conference on fuzzy systems, pp 997–1000

  12. Chang BCH, Halgamuge SK (2002) Protein motif extraction with neuro-fuzzy optimisation. Bioinformatics 18(8):1084–1090

    Article  Google Scholar 

  13. Boilot P, Hines EL, Gardner JW (2001) Knowledge extraction from electronic nose data sets using hybrid neuro-fuzzy systems. Sensors Update 8(1):73–94

    Article  Google Scholar 

  14. Han JG, Chi KH, Yeon YK (2005) Land cover classification of IKONOS multispectral satellite data: neuro-fuzzy, neural network and maximum likelihood methods. Rough sets, fuzzy sets, data mining, and granular computing. Springer, Berlin, pp 251–262

  15. Piramuthu S (1999) Financial credit-risk evaluation with neural and neurofuzzy systems. Eur J Oper Res 12(2):310–321

    Article  Google Scholar 

  16. Wang LX (1996) A course in fuzzy systems and control. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  17. Zhou J, Raju GVS, Kisner RA (1991) Hierarchical fuzzy control. Int J Contr 54(5):1201–1216

    Article  MATH  MathSciNet  Google Scholar 

  18. Yager RR (1993) On Hierarchical Structure for Fuzzy Modeling and Control. IEEE Trans Syst Man Cybern 23(4):1189–1197

    Article  Google Scholar 

  19. Yager RR (1998) On the construction of hierarchical fuzzy systems models. IEEE Trans Syst Man Cybern C 28(1):55–66

    Article  Google Scholar 

  20. Wang LX (1999) Analysis and design of hierarchical fuzzy systems. IEEE Trans Fuzzy Syst 7(5):617–624

    Article  Google Scholar 

  21. Rattasiri W, Halgamuge SK (2003) Computationally advantageous and stable hierarchical fuzzy systems for active suspension. IEEE Trans Ind Electron 50(1):48–61

    Article  Google Scholar 

  22. Nowicki R, Scherer R, Rutkowski L (2003) A hierarchical neuro-fuzzy system based on S-implications. Proc Int Joint Conf Neural Netw 1:321–325

    Google Scholar 

  23. Figueiredo K, Vellasco M, Pacheco M, Souza F (2004) Reinforcement learning hierarchical neuro-fuzzy Politree Model for control of autonomous agents. In: Proceedings of fourth international conference on hybrid intelligent systems, pp 130–135

  24. Rashidy H, Saafan A, Rezeka S, Awad T (2003) A hierarchical neuro-fuzzy system for identification of simultaneous faults in hydraulic servovalves. In: Proceedings of American contr. conference, pp 4269–4274

  25. Shim JY, Lim GY, Hwang CS (1999) Selective neurofuzzy system with fuzzy associative matrix. In: Proceedings of IEEE international fuzzy system conference, pp 1671–1674

  26. Saad R, Halgamuge SK (2004) Stability of hierarchical fuzzy systems generated by neuro-fuzzy. J Soft Comput Fusion Found Methodol Appl 8(6):409–416

    Article  Google Scholar 

  27. Kim J, Zeigler BP (1996) Designing fuzzy logic controllers using a multiresolutional search paradigm. IEEE Trans Fuzzy Syst 4(3):213–226

    Article  Google Scholar 

  28. Nurnberger A (2003) A hybrid approach to learn recurrent fuzzy systems. In: Proceedings of international conference on NAFIPS, pp 353–358

  29. Halgamuge SK (1997) Self-evolving neural networks for rule-based data processing. IEEE Trans Signal Process 45(11):2766–2773

    Article  Google Scholar 

  30. Halgamuge SK (2003) Realtime classification with structure adapting neuro-fuzzy systems. J Integr Comput Aided Eng 10(4):357–367

    Google Scholar 

  31. Fisher RA (1936) Iris Plant Database. ftp://ftp.ics.uci.edu/pub/machine-learning-databases/iris/

  32. Siegler RS (1994) Balance Scale Database. http://www.ics.uci.edu/mlearn/databases/balance-scale/

  33. Loh WY, Lim TS (1997) Teaching assistant evaluation. ftp://ftp.ics.uci.edu/pub/machine-learning-databases/tae/

  34. Forsyth RS, Turney P (1990) Liver-disorders Database. ftp://ftp.ics.uci.edu/pub/machine-learning-databases/liver-disorders/

  35. German B, Spiehler V (1987) Glass Identification Database. ftp://ftp.ics.uci.edu/pub/machine-learning-databases/glass/

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Melbourne School of Engineering, University of Melbourne, Parkville, VIC, 3010, Australia

    Waratt Rattasiri & Saman K. Halgamuge

  2. Department of Electrical Engineering, University of Moratuwa, Moratuwa, Katubadda, 10400, Sri Lanka

    Nalin Wickramarachchi

Authors
  1. Waratt Rattasiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saman K. Halgamuge
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nalin Wickramarachchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Waratt Rattasiri.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rattasiri, W., Halgamuge, S.K. & Wickramarachchi, N. Structure adaptation of hierarchical knowledge-based classifiers. Neural Comput & Applic 18, 523–537 (2009). https://doi.org/10.1007/s00521-008-0190-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-008-0190-6

Keywords

Search

Navigation