Skip to main content
SpringerLink
Log in

Rule Extraction from DEWNN to Solve Classification and Regression Problems

  • Conference paper
Swarm, Evolutionary, and Memetic Computing (SEMCCO 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7677))

Included in the following conference series:

Abstract

This paper proposes a method to extract rules from differential evolution trained wavelet neural network (DEWNN) [1]. for solving classification and regression problems. The rule generation methods viz., Decision Tree (DT), Ripper and Classification and Regression Tree (CART) and Dynamic Evolving Neuro Fuzzy Inference System (DENFIS) are employed to extract rules from DEWNN for classification and regression problems respectively. The feature selection algorithm adapted by Chauhan et al., [1] is used in the present study. The effectiveness of the proposed hybrid is evaluated on Iris, Wine and four bankruptcy prediction datasets namely Spanish banks, Turkish banks, US banks, UK banks and Auto MPG dataset, Body fat dataset, Boston Housing dataset, Forest Fires dataset, Pollution dataset, by using 10-fold cross validation. From the results, it is concluded that the proposed hybrid method performed well in terms of sensitivity in classification problems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chauhan, N., Ravi, V., Karthikchandra, D.: Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks. Expert Systems with Application 36, 7659–7665 (2009)

    Article  Google Scholar 

  2. Naveen, N., Ravi, V., Raghavendra Rao, C., Chauhan, N.: Differential evolution trained radial basis function network: application to bankruptcy prediction. International Journal of Bio-Inspired Computation 2(3/4), 222–232 (2010)

    Article  Google Scholar 

  3. Andrews, R., Diederich, J., Tickle, A.B.: A survey and critique of techniques for extracting rules from trained artificial neural networks. Know. Based Systems 8(6), 373–389 (1996)

    Article  Google Scholar 

  4. Gallant, S.I.: Connectionist expert systems. Communications of the ACM 31(2), 152–169 (1988)

    Article  Google Scholar 

  5. Fu, L.M.: Rule generation from neural networks. IEEE Transactions on Systems, Man and Cybernetics 24(8), 1114–1124 (1994)

    Article  Google Scholar 

  6. Towlell, G.G., Shavlik, J.W.: The extraction of refined rules from knowledge-based neural networks. Machine Learning 13(1), 71–101 (1993)

    Google Scholar 

  7. Arbatli, A.D., Akin, H.L.: Rule extraction from trained neural networks using genetic algorithms. Nonlinear Analysis, Theory, Methods & Applications 30(3), 1639–1648 (1997)

    Article  MATH  Google Scholar 

  8. Fan, Y., Li, C.-J.: Diagnostic rule extraction from trained feed forward neural networks. Mechanical Systems and Signal Processing 16(6), 1073–1081 (2002)

    Article  Google Scholar 

  9. Krishnan, R., Sivakumar, G., Bhattacharya, P.: A search technique for rule extraction from trained neural networks. Pattern Recognition Letters 20, 273–280 (1999)

    Article  MATH  Google Scholar 

  10. Zhang, X., Qi, J., Zhang, R., Liu, M., Hu, Z., Xue, H.: Prediction of programmed-temperature retention values of naphthas by wavelet neural networks. Computers and Chemistry 25, 25–133 (2001)

    Article  Google Scholar 

  11. Avci, E.: An expert system based on wavelet neural network-adaptive norm entropy for scale invariant texture classification. Expert Systems with Applications 32, 919–926 (2007)

    Article  Google Scholar 

  12. Lung, S.-Y.: Efficient text independent speaker recognition with wavelet feature selection based multilayered neural network using supervised learning algorithm. Pattern Recognition 40, 3616–3620 (2007)

    Article  MATH  Google Scholar 

  13. Yu, S.-N., Chen, Y.H.: Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognition Letters 28, 1142–1150 (2007)

    Article  Google Scholar 

  14. Rajkiran, N., Ravi, V.: Software reliability prediction using wavelet neural networks. In: International Conference on Computational Intelligence and Multimedia Applications, Sivakasi, Tamilnadu, India (2007)

    Google Scholar 

  15. Dimoulas, C., Kalliris, G., Papanikolaou, G., Petridis, V., Kalampakas, A.: Bowel-sound pattern analysis using wavelets and neural networks with application to long-term, unsupervised, gastrointestinal motility monitoring. Expert Systems with Applications 34, 26–41 (2008)

    Article  Google Scholar 

  16. Dong, L., Xiao, D., Liang, Y., Liu, Y.: Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers. Electric Power Systems Research 78, 129–136 (2008)

    Article  Google Scholar 

  17. Pan, C., Chen, W., Yun, Y.: Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network. IET Electric Power Applications 2(1), 71–76 (2008)

    Article  Google Scholar 

  18. Vinaykumar, K., Ravi, V., Carr, M., Raj Kiran, N.: Software cost estimation using wavelet neural networks. Journal of Systems and Software 8(11), 1853–1867 (2008)

    Article  Google Scholar 

  19. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1992)

    Google Scholar 

  20. Cohen, W.W.: Fast Effective Rule Induction, From Machine Learning. In: Proceedings of the Twelfth International Conference, ML 1995 (1995)

    Google Scholar 

  21. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)

    MATH  Google Scholar 

  22. Kasabov, N., Song, Q.: DENFIS: Dynamic, evolving neural-fuzzy inference systems and its application for time-series prediction. IEEE Transactions on Fuzzy Systems 10, 144–154 (2002)

    Article  Google Scholar 

  23. Olmeda, I., Fernandez, E.: Hybrid classifiers for financial multicriteria decision making: The case of bankruptcy prediction. Comp. Economics 10, 317–335 (1997)

    Article  MATH  Google Scholar 

  24. Canbas, S., Caubak, B., Kilic, S.B.: Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. European Journal of Operational Research 166, 528–546 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  25. Beynon, M.J., Peel, M.J.: Variable Precision Refought Set Theory and Data Discretisation: An Application to Corporate Failure Prediction. Omega 29, 561–576 (2001)

    Article  Google Scholar 

  26. Rahimian, E., Singh, S., Thammachote, T., Virmani, R.: Bankruptcy prediction by neural network. In: Trippi, R.R., Turban, E. (eds.) Neural Networks in Finance and Investing, Irwin Professional Publishing, Burr Ridge (1996)

    Google Scholar 

  27. Asuncion, A., Newman, D.J.: UCI machine learning repository. University of California, School of Information and Computer Science, Irvine (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Development and Research in banking Technology, Castle Hills, #1, Masab Tank, Hyderabad, 500057, A P, India

    Nekuri Naveen & Vadlamani Ravi

  2. Department of Computer & Information Sciences, University of Hyderabad, Hyderabad, 500046, A P, India

    Nekuri Naveen & Chillarige Raghavendra Rao

Authors
  1. Nekuri Naveen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vadlamani Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chillarige Raghavendra Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, 110016, Delhi, India

    Bijaya Ketan Panigrahi

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electronics and Telecom. Engineering, Institute of Technical Education & Research, Siksha ’O’ Anusandhan University, 751030, Bhubaneswar, Odisha, India

    Pradipta Kumar Nanda

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Naveen, N., Ravi, V., Rao, C.R. (2012). Rule Extraction from DEWNN to Solve Classification and Regression Problems. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35380-2_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35379-6

  • Online ISBN: 978-3-642-35380-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation