Skip to main content
SpringerLink
Log in

Fuzzy classification in dynamic environments

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

Abstract

The persistence and evolution of systems essentially depend on their adaptivity to new situations. As an expression of intelligence, adaptivity is a distinguishing quality of any system that is able to learn and to adjust itself in a flexible manner to new environmental conditions and such ability ensures self-correction over time as new events happen, new input becomes available, or new operational conditions occur. This requires self-monitoring of the performance in an ever-changing environment. The relevance of adaptivity is established in numerous domains and by versatile real-world applications. The present paper presents an incremental fuzzy rule-based system for classification purposes. Relying on fuzzy min–max neural networks, the paper explains how fuzzy rules can be continuously online generated to meet the requirements of non-stationary dynamic environments, where data arrives over long periods of time. The approach proposed to deal with an ambient intelligence application. The simulation results show its effectiveness in dealing with dynamic situations and its performance when compared with existing approaches.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Notes

  1. Actually it is not a similarity measure, since it does not satisfy the symmetry property, but it is referred to so just in the sense of closeness

  2. With acknowledgment to Dr. Hagras for furnishing this study with the iDorm data.

References

  • Angelov P (2004) An approach for fuzzy rule-base adaptation using on-line clustering. Int J Approx Reason 35(3):275–289

    Article  MATH  MathSciNet  Google Scholar 

  • Angelov P, Lughoferb E, Zhou X (2008) Evolving fuzzy classifiers using different model architectures. Fuzzy Sets Syst 159:3160–3182

    Article  MATH  Google Scholar 

  • Bouchachia A (2004) Incremental rule learning using incremental clustering. In: Proceedings of the 10th conference on information processing and management of uncertainty in knowledge-based systems, vol 3, pp 2085–2092

  • Bouchachia A (2004) Maintaining knowledge bases via incremental rule learning. In: Proceedings of the international workshop on soft computing for information mining, pp 51–63

  • Bouchachia A (2006) Incremental learning via function decomposition. In: Proceedings of the international conference on machine learning and applications, pp 63–68

  • Bouchachia A (2009) Encyclopedia of data warehousing and mining, chapter incremental learning, 2nd edn. Idea-Group, pp 1006–1012

  • Bouchachia A, Gabrys B, Sahel Z (2007) Overview of some incremental learning algorithms. In: Proceedings of the 2007 IEEE international conference on fuzzy systems, pp 1–6

  • Bouchachia A, Mittermeir R (2006) Towards fuzzy incremental classifiers. Soft Comput 11(2):193–207

    Article  Google Scholar 

  • Carpenter G, Grossberg S, Rosen D (1991) Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw 4(6):759–771

    Article  Google Scholar 

  • de Barros J, Dexter L (2007) On-line identification of computationally undemanding evolving fuzzy models. Fuzzy Sets Syst 158(16):1997–2012

    MATH  Google Scholar 

  • French R (1999) Catastrophic forgetting in connectionist networks: causes, consequences and solutions. Trends Cogn Sci 3(4):128–135

    Article  MathSciNet  Google Scholar 

  • Fritzke B (1995) A growing neural gas network learns topologies. In: Advances in neural information processing systems, pp 625–632

  • Gabrys B (2002) Agglomerative learning algorithms for general fuzzy min–max neural network. J VLSI Signal Process Syst 32(1):67–82

    Article  MATH  Google Scholar 

  • Gabrys B, Bargiela A (2000) General fuzzy min–max neural network for clustering and classification. IEEE Trans Neural Netw 11(3):769–783

    Article  Google Scholar 

  • Gama J, Medas P, Castillo G, Pereira Rodrigues P (2004) Learning with drift detection. In: Advances in artificial intelligence—17th Brazilian symposium on artificial intelligence, pp 286–295

  • Grossberg S (1988) Nonlinear neural networks: principles, mechanism, and architectures. Neural Netw 1:17–61

    Article  Google Scholar 

  • Hagras H, Doctor F, Lopez A, Callaghan V (2007) An incremental adaptive life long learning approach for type-2 fuzzy embedded agents in ambient intelligent environments. IEEE Trans Fuzzy Syst 15(1):41–55

    Article  Google Scholar 

  • Kasabov N (2001) On-line learning, reasoning, rule extraction and aggregation in locally optimized evolving fuzzy neural networks. Neurocomputing 41:25–45

    Article  MATH  Google Scholar 

  • Klinkenberg R (2004) Learning drifting concepts: example selection vs. example weighting. Intell Data Anal 8(3):281–300

    Google Scholar 

  • Kuncheva L (2004) Classifier ensembles for changing environments. In: Proceedings of the fifth international workshop on multiple classifier systems, pp 1–15

  • Liu F, Quek C, Ng G (2007) A novel generic hebbian ordering-based fuzzy rule base reduction approach to mamdani neuro-fuzzy system. Neural Comput 19(6):1656–1680

    Article  MATH  Google Scholar 

  • McCloskey M, Cohen N (1999) Catastrophic interference in connectionist networks: the sequential learning problem. Psychol Learn Motivation 24:109–164

    Article  Google Scholar 

  • Pedrycz W, Gomide F (1998) Introduction to fuzzy sets: analysis and design. MIT Press, Cambridge

  • Ratcliff R (1990) Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. Psychol Rev 97:285–308

    Article  Google Scholar 

  • Sahel Z, Bouchachia A, Gabrys B (2007) Adaptive mechanisms for classification problems with drifting data. In: Proceedings of the 11th international conference on knowledge-based intelligent information and engineering systems (KES’07), LNCS 4693, pp 419–426

  • Salzberg S (1991) A nearest hyperrectangle learning method. Mach Learn 6:277–309

    Google Scholar 

  • Sharkey N, Sharkey A (1995) Catastrophic forgetting in connectionist networks: causes, consequences and solutions. Anal Catastrophic Interference 7(3–4):301–329

    Google Scholar 

  • Tsymbal A (2004) The problem of concept drift: definitions and related work. Technical Report TCD-CS-2004-15, Department of Computer Science Trinity College, Dublin

  • Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23:69–101

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, University of Klagenfurt, Klagenfurt, Austria

    Abdelhamid Bouchachia

Authors
  1. Abdelhamid Bouchachia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdelhamid Bouchachia.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bouchachia, A. Fuzzy classification in dynamic environments. Soft Comput 15, 1009–1022 (2011). https://doi.org/10.1007/s00500-010-0657-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-010-0657-0

Keywords

Search

Navigation