Skip to main content
Log in

Positive and negative fuzzy rule system, extreme learning machine and image classification

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

We often use the positive fuzzy rules only for image classification in traditional image classification systems, ignoring the useful negative classification information. Thanh Minh Nguyen and QMJonathan Wu introduced the negative fuzzy rules into the image classification, and proposed combination of positive and negative fuzzy rules to form the positive and negative fuzzy rule system, and then applied it to remote sensing image/natural image classification. Their experiments demonstrated that their proposed method has achieved promising results. However, since their method was realized using the feedforward neural network model which requires adjusting the weights in the gradient descent way, the training speed is very slow. Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFNs) learning algorithm, which has distinctive advantages such as quick learning, good generalization performance. In this paper, the equivalence between ELM and the positive and negative fuzzy rule system is revealed, so ELM can be naturally used for training the positive and negative fuzzy rule system quickly for image classification. Our experimental results indicate this claim.

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

Access this article

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

Similar content being viewed by others

References

  1. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  2. Nguyen TM, Wu J-QM (2008) A combination of positive and negative fuzzy rules for image classification problem. In: Proceedings of the 2008 seventh international conference on machine learning and applications, pp 741–746

  3. Tang Y, Yan P, Yuan Y et al (2011) Single-image super-resolution via local learning. Int J Mach Learn Cybern 2(1):15–23

    Article  Google Scholar 

  4. Kang S, Park S (2009) A fusion neural network classifier for image classification. Pattern Recogn Lett 30(9):789–793

    Article  MathSciNet  Google Scholar 

  5. Tzeng YC, Chen KS (1998) A fuzzy neural network to SAR image classification. IEEE Trans Geosci Remote Sens 36:301–307

    Article  Google Scholar 

  6. Zhou W-Y (1999) Verification of the nonparametric characteristics of backpropagation neural networks for image classification. IEEE Trans Geosci Remote Sens 37(1):771–779

    Article  Google Scholar 

  7. Nakashima T, Schaefer G, Yokota Y, Ishibuchi H (2007) A weighted fuzzy classifier and its application to image processing tasks. Fuzzy Sets Syst 158:284–294

    Article  MathSciNet  Google Scholar 

  8. de Moraes RM, Banon GJF, Sandri SA (2002) Fuzzy expert systems architecture for image classification using mathematical morphology operators. Inf Sci 142(1):7–21

    Article  MATH  Google Scholar 

  9. Liu DM, Wang ZX (2008) A united classification system of X-ray image based on fuzzy rule and neural networks. In: 3rd international conference on intelligent system and knowledge engineering, ISKE 2008, pp 1717–1722

  10. Mandai DP, Murthy CA, Pal SK (1992) Formulation of a multivalued recognition system. IEEE Trans Syst Man Cybern 22(4):607–620

    Article  Google Scholar 

  11. Pal S, Mandai DP (1992) Linguistic recognition system based on approximate reasoning. Inf Sci 61:135–161

    Article  Google Scholar 

  12. Ishibuchi H, Yamamoto T (2005) Rule weight specification in fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 13(4):428–435

    Article  Google Scholar 

  13. Tong DL, Mintram R (2010) Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection. Int J Mach Learn Cybern 1(1–4):75–87

    Article  Google Scholar 

  14. Yi W, Lu M, Liu Z (2011) Multi-valued attribute and multi-labeled data decision tree algorithm. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0015-2

  15. Yu SW, Zhu KJ, Diao FQ (2008) A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction. Appl Math Comput 195:66–75

    Article  MathSciNet  MATH  Google Scholar 

  16. Branson JS, Lilly JH (1999) Incorporation of negative rules into fuzzy inference systems. In: Proceedings of the 38th IEEE Conference on Decision and Control, vol 5, pp 5283–5288

  17. Lilly JH (2007) Evolution of a negative-rule fuzzy obstacle avoidance controller for an autonomous vehicle. IEEE Trans Fuzzy Syst 15(4):718–728

    Article  MathSciNet  Google Scholar 

  18. Li Y, Deng J-M, Wei M-Y (2002) Meaning and precision of adaptive fuzzy systems with Gaussian-type membership functions. Fuzzy Sets Syst 127:85–97

    Article  MathSciNet  Google Scholar 

  19. Bartlett PL (1996) For valid generalization, the size of weights is more important than the size of networks. Adv Neural Inform Process Syst 9(1):134–140

    MathSciNet  Google Scholar 

  20. Mitra P, Shankar BU, Pall SK (2004) Segmentation of multispectral remote sensing images using active support vec tor machines. Pattern Recogn Lett 25:1067–1074

    Article  Google Scholar 

  21. Fu Y, Wang Y-W, Wang W-Q, Gao W (2003) Content-based natural image classification and retrieval using SVM. Chin J Comput 26(10):1261–1265

    Google Scholar 

  22. Guo Y-H, Cheng D (2009) New neutrosophic approach to image segmentation. Pattern Recogn 42(5):587–595

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Hong Kong Polytechnic University under Grant 1-ZV5V, by the National Natural Science Foundation of China under Grants 60903100, 60975027 and 90820002, and by the Natural Science Foundation of Jiangsu province under Grant BK2009067.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wang Shitong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jun, W., Shitong, W. & Chung, Fl. Positive and negative fuzzy rule system, extreme learning machine and image classification. Int. J. Mach. Learn. & Cyber. 2, 261–271 (2011). https://doi.org/10.1007/s13042-011-0024-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-011-0024-1

Keywords

Navigation