[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Rough sets for adapting wavelet neural networks as a new classifier system

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Classification is an important theme in data mining. Rough sets and neural networks are two techniques applied to data mining problems. Wavelet neural networks have recently attracted great interest because of their advantages over conventional neural networks as they are universal approximations and achieve faster convergence. This paper presents a hybrid system to extract efficiently classification rules from decision table. The neurons of such hybrid network instantiate approximate reasoning knowledge gleaned from input data. The new model uses rough set theory to help in decreasing the computational effort needed for building the network structure by using what is called reduct algorithm and a rules set (knowledge) is generated from the decision table. By applying the wavelets, frequencies analysis, rough sets and dynamic scaling in connection with neural network, novel and reliable classifier architecture is obtained and its effectiveness is verified by the experiments comparing with traditional rough set and neural networks approaches.

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
£29.99 /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 includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Abiyev H (2004) Fuzzy wavelet neural network for control of dynamic plants. Int J Comput Intell 1(2):1304–4508

    Google Scholar 

  2. Avci E (2007) Performance comparison of wavelet families for analog modulation classification using expert discrete wavelet neural network system. Expert Syst Appl 33:23–35

    Article  Google Scholar 

  3. Dong W, Zhao Y, Wang J, Gu SH (2005) New approach to structure optimization of wavelet neural network based on rough sets theory. Int J Inf Syst Sci 1(3):382–389

    MATH  Google Scholar 

  4. Duch W, Setiono R, Zurada J (2004) Computational intelligence methods for rule-based data understanding. Proc IEEE 92(5):771–805

    Article  Google Scholar 

  5. Egawa S, Suyama K, Yoichi A, Matsumoto K, Tsukayama C, Kuwao S, Baba S (2001) A study of pretreatment nomograms to predict pathological stage and biochemical recurrence after radical prostatectomy for clinically respectable prostate cancer in Japanese Men. Jpn J Clin Oncol 31(2):74–81

    Article  Google Scholar 

  6. Esen H, Ozgen F, Esen M, Sengur A (2009) Artificial neural network and wavelet neural network approaches for modeling of a solar air heater. Expert Syst Appl 36:11240–11248

    Article  Google Scholar 

  7. Han M, Yin J (2008) The hidden neurons selection of the wavelet networks using support vector machines and ridge regression. Neurocomputing 72:471–479

    Article  Google Scholar 

  8. Hassan Y, Tazaki E, Egawa S, Suyama K (2002) Decision making using hybrid rough sets and neural networks. Int J Neural Syst 12(6):435–446

    Article  Google Scholar 

  9. Hassan Y, Tazaki E (2005) Emergent rough set data analysis. In: Peters JF (ed) Transactions on rough sets II: rough sets and fuzzy sets. Springer, Berlin, pp 343–361

    Google Scholar 

  10. Li R, Wang Z (2004) Mining classification rules using rough sets and neural networks. Eur J Oper Res 157:439–448

    Article  MATH  Google Scholar 

  11. Pawlak Z (2004) Some issues on rough sets. Trans Rough Sets 1:1–58

    Article  MathSciNet  Google Scholar 

  12. Pawlak Z Skowron A (2007) Rough sets: some extensions. Inf Sci 177(1):28–40

    Article  MathSciNet  MATH  Google Scholar 

  13. Sahoo D, Dulikravich G (2006) Evolutionary wavelet neural network for large scale function estimation in optimization. In: 11th AIAA/ISSMO multidisciplinary analysis and optimization conference, pp 55–69

  14. Subasi A, Yilmaz M, Ozcalik H (2006) Classification of EMG signals using wavelet neural network. J Neurosci Methods 156:360–367

    Article  Google Scholar 

  15. Thangavel K, Pethalakshmi A (2009) Dimensionality reduction based on rough set theory: a review. Appl Soft Comput 9:1–12

    Article  Google Scholar 

  16. Wei J (2003) Rough set based approach to selection of node. Int J Comput Cogn 1(2):25–40

    Google Scholar 

  17. Zhao D, Fang X, Sun S, Wang J, Gu Sh (2006) Sims designing based on economic forecasting model with wavelet neural networks. Int J Inf Syst Sci 2(1):67–74

    MATH  Google Scholar 

  18. Zhu W, Wang F (2007) On three types of covering-based rough sets. IEEE Trans Knowl Data Eng 19(8):1131–1144

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics (Computer Science), Alexandria University, Alexandria, Egypt

    Yasser F. Hassan

Authors
  1. Yasser F. Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasser F. Hassan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hassan, Y.F. Rough sets for adapting wavelet neural networks as a new classifier system. Appl Intell 35, 260–268 (2011). https://doi.org/10.1007/s10489-010-0218-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-010-0218-3

Keywords

Search

Navigation