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Intelligent system based on local linear wavelet neural network and recursive least square approach for breast cancer classification

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Abstract

A new learning technique for local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of Error Back propagation and Recursive Least Square (RLS) is introduced for training the parameters of LLWNN. The variance and centers of LLWNN are updated using back propagation and weights are updated using Recursive Least Square (RLS). Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification.

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References

  • Al-Obaidy M, Ayesh A, Sheta AF (2008) Optimizing the communication distance of an ad hoc wireless sensor networks by genetic algorithms. Artif Intell Rev 29(3–4): 183–194. doi:10.1007/s10462-009-9148-z

    Article  Google Scholar 

  • An Introduction to Kalman Filter by Welch and Bishop, Welch@cs.unc.edu, http://ww.cs.unc.edu/~welch

  • Araujo L (2007) How evolutionary algorithms are applied to statistical natural language processing. Artif Intell Rev 28(4): 275–303. doi:10.1007/s10462-009-9104-y

    Article  MathSciNet  Google Scholar 

  • Chen S, Wu Y, Luk B (1999) Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Trans Neural Netw 10(5): 1239–1243

    Article  Google Scholar 

  • Chen YH et al (2000) Evolving wavelet neural networks for system identification. In: Proceeding of international conference on electrical engineering, pp 279–282

  • Chen YH et al (2001) Evolving the basis function neural networks for system identification. Int J Adv Comput Intell 5(4): 229–238

    MATH  Google Scholar 

  • Chen Y, Dong J, Yang B, Zhang Y (2004) Local linear wavelet neural network. Fifth world congress on intelligent control and automation (WCIA), Hangzhou, pp 1954–1957

  • Chen Y, Yang Bo, Dong J (2006) Time series prediction using a local linear wavelet neural network. Neuro Comput 69: 449–465

    Google Scholar 

  • Karayiannis N (1999) Reformulated radial basis neural networks trained by gradient descent. IEEE Trans Neural Netw 10(3): 657–671

    Article  Google Scholar 

  • Kawaji S, Chen YH (2001) Evolving neuro fuzzy system by hybrid soft computing approaches for system identification. Int J Adv Comput Intel 5(4): 229–238

    Google Scholar 

  • Kennedy J, Eberhart RC, Shi Y (1995) Particle swarm optimization. Proc IEEE Int J Conf Neural Netw 4: 1942–1948

    Article  Google Scholar 

  • Kermani BG, White MW, Nagle HT (1995) Feature extraction by genetic algorithm for neural networks in breast cancer classification. In: Engineering in Medicine and Biology Society, 17th Annual Conference, IEEE, vol 1, pp 831–832, doi:10.1109/IEMBS.1995.575385

  • McGarry K, Tait J, Wermter S (1999) Rule extraction from radial basis function networks. Proceedings of IEEE conference on Artificial Neural Networks, University of Edinburg, UK, September 7–10, pp 613–618

  • Samantaray SK, Dash PK, Panda G (2006) Fault classification and location using HS_transform and radial basis function neural network. Electrical power system research. Elsevier, pp 897–905

  • Sum J, Leung C-s, Young GH, Kan W-k (1999) Kalman filtering method in neural-network, training and pruning. IEEE Trans Neural Netw 10(1): 161–166

    Article  Google Scholar 

  • V D Sanchez A (ed) (1994) Special issue on Backpropagation, Vol 6, Part II, Neurocomputing

  • V D Sanchez A (ed) (1998) Special issue on RBF network, Vol 19, Part I, Neurocomputing

  • Wang T et al (2000) A wavelet neural network for the approximation of nonlinear multivariable function. Trans Inst Electron Eng C 102-C: 185–193

    Google Scholar 

  • Yang X, Pang G, Yung N (2004) Discriminative training approaches to fabric defect classification based on wavelet transform. Pattern Recogn 37: 889–899

    Article  Google Scholar 

  • Zhang Q, Benveniste A (1992) Wavelet Networks. IEEE Trans Neural Netw 3(6): 889–898

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Gandhi Engineering College, Biju Patnaik University of Technology, Rourkela, India

    M. R. Senapati

  2. S ‘O’ A University, Bhubaneswar, Orissa, India

    P. K. Dash

Authors
  1. M. R. Senapati
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  2. P. K. Dash
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Correspondence to M. R. Senapati.

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Senapati, M.R., Dash, P.K. Intelligent system based on local linear wavelet neural network and recursive least square approach for breast cancer classification. Artif Intell Rev 39, 151–163 (2013). https://doi.org/10.1007/s10462-011-9263-5

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  • DOI: https://doi.org/10.1007/s10462-011-9263-5

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