Improved Wavelet Neural Networks for Early Cancer Diagnosis Using Clustering Algorithms
Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K-means (KM), Fuzzy C-means (FCM), symmetry-based K-means (SBKM), symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-based K-means (MPKM) clustering algorithms in choosing the translation parameter of a WNN. These modified WNNs are further applied to the heterogeneous cancer classification using benchmark microarray data and were compared against the conventional WNN with random initialization method. Experimental results showed that a WNN classifier with the MPKM algorithm is more precise than the conventional WNN as well as the WNNs with other clustering algorithms.
[1] E. Gómez-Ramírez, K. Najim and E. Ikonen, "Forecasting time series
with a new architecture for polynomial artificial neural network,"
Applied Soft Computin, vol.7, pp.1209-1216, 2007.
[2] H. Zhang, B. Zhang, W. Huang and Q. Tian, "Gabor wavelet associative
memory for face recognition," IEEE Transactions on Neural Networks,
vol. 16, pp. 275-278, 2005.
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networks with novel unsupervised training method for numerical
solution of the partial differential equations," Applied Soft Computing,
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neural networks," International Journal of Systems Applications,
Engineering & Development, vol. 1, pp. 173-178, 2007.
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its application in neuro-fuzzy models," Applied Soft Computing, vol. 8,
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[8] B. Biswal, P.K. Dash, B.K. Panigrahi and J.B.V. Reddy, "Power signal
classification using dynamic wavelet network," Applied Soft Computing,
vol. 9, pp. 118-125, 2009.
[9] C.J. Lin, "Nonlinear systems control using self-constructing wavelet
networks," Applied Soft Computing, vol. 9, pp. 71-79, 2009.
[10] W. Maoan, J. Shijiu, W. Likun and Z. Van, "Defect characteristic
prediction of pipeline by means of wavelet neural network based on the
hierarchical clustering algorithm (Published Conference Proceedings
style)," Proceedings of Biennial International Pipeline Conference,
Alberta, 2004, pp. 921-924.
[11] Z. Xiao-Guang, K. Ying-Zhi, G. Dao-Hua and W. Xing Biao, "Fuzzy
wavelet neural networks based on SVM," Journal of East China
University of Science and Technology, vol. 32, no. 11, pp. 1351-1354,
2006.
[12] K. Seong-Ju, K. Yong-Taek, S. Jae-Yong and J. Hong-Tae, "Design of
the scaling-wavelet neural network using genetic algorithm (Published
Conference Proceedings style)," Proceedings of the International Joint
Conference on Neural Networks, Honolulu , 2002, pp. 2174-2179.
[13] Y. Oussar and G. Dreyfus, "Initialization by selection for wavelet
network training," Neurocomputing, vol. 34, pp. 131-143, 2000.
[14] Z. Dahai, B. Yanqiu, Y.B. Bi and Y. Sun, "Design and initialization
algorithm based on modulus maxima of wavelet transform for wavelet
neural network (Published Conference Proceedings style)," International
Conference on Power System Technology, Singapore, 2004, pp. 897-
901.
[15] Q. Zhang and A. Beveniste, "Wavelet networks," IEEE Transactions on
Neural Networks, vol. 3, pp. 889-898, 1992.
[16] C. Jiacong and L. Xingchun, "Application of the diagonal recurrent
wavelet neural network to solar irradiation forecast assisted with fuzzy
technique," Engineering Applications of Artificial Intelligence, vol. 21,
pp. 1255-1263, 2008.
[17] E. Avci and D. Avci, "The performance comparison of discrete wavelet
neural network and discrete wavelet adaptive network based fuzzy
inference system for digital modulation recognition," Expert Systems
with Application, vol. 35, pp. 90-101, 2008.
[18] L. Jie, L. Dai-fei, D. Xue-ru, Z. Zhong and D. Feng-qi, "Prediction of
AI(OH)3 fluidized roasting temperature based on wavelet neural
network," Transactions of Nonferrous Metals Society of China, vol. 17,
pp. 1052-1056, 2007.
[19] K. Vinay Kumar, V. Ravi, Mahil Carr and N. Raj Kiran, "Software
development cost estimation using wavelet neural networks," The
Journal of Systems and Software, vol. 81, pp. 1853-1867, 2008.
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unpublished
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algorithm with a distance based on cluster symmetry", IEEE
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[22] S. Mu-Chun, C. Chen-Hsing and H. Chen-Chiung, "Fuzzy C-means
algorithm with a point symmetry distance," International Journal of
Fuzzy Systems, vol. 7, no. 4, pp. 175-181, 2005.
[23] C. Kuo-Liang and L. Jhin-Sian, "Faster and more robust point
symmetry-based K-means algprithm," Pattern Recognition, vol. 40, pp.
410-422, 2007.
[24] D. Amaratunga and J. Cabrera, Exploration and analysis of DNA
microarray and protein array data. John Wiley & Sons: New Jersey, pp.
123-126, 2004.
[25] T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P.
Mesirov, H. Coller, M.L. Loh, J.R. Downing, M.A. Caligiuri, C.D.
Bloomfield and E.S. Lander, "Molecular classification of cancer: Class
discovery and class prediction by gene expression monitoring," Science,
vol. 286, pp. 531-537, 1999.
[26] J. Khan, S.J. Wei, M. Ringnér, L.H. Saal, M. Ladanyi, F. Westermann,
F. Berthold, M. Schwab, C.R. Antonescu, C. Peterson and P. Meltzer,
"Classification and diagnostic prediction of cancers using gene
expression profiling and artificial neural network," Nature Med, vol. 7,
pp. 673-679, 2001.
[27] C.L. Nutt, D.R. Mani, R.A. Betensky, P. Tamayo, J.G. Cairncross, C.
Ladd, U. Pohl, C. Hartmann, M.E. McLaughlin, T.T. Batchelor, P.M.
Black, A.V. Deimling, A.L. Pomeroy, T.R. Golub and D.N. Louis,
"Gene expression-based classification of malignant gliomas correlates
better with survival than histological classification," Cancer Res., vol.
63, pp. 1602-1607, 2003.
[28] S.L. Pomeroy, P. Tamayo, M. Gaasenbeek, L.M. Sturla, M. Angelo,
M.E. McLaughlin, J.Y. Kim, L.C. GoumnerovaC, P.M. Black, C. Lau,
J.C. Allen, D. Zagzag, J.M. Olson, T. Curran, C. Wetmore, J.A. Biegel,
T. Poggio, S.E.S. Lander and T.R. Golub, "Prediction of central nervous
system embryonal tumor outcome based on gene expression," Nature,
vol. 415, pp. 436-442, 2002.
[1] E. Gómez-Ramírez, K. Najim and E. Ikonen, "Forecasting time series
with a new architecture for polynomial artificial neural network,"
Applied Soft Computin, vol.7, pp.1209-1216, 2007.
[2] H. Zhang, B. Zhang, W. Huang and Q. Tian, "Gabor wavelet associative
memory for face recognition," IEEE Transactions on Neural Networks,
vol. 16, pp. 275-278, 2005.
[3] S. Srivastava, M. Singh, M. Hanmandlu and A.N. Jha, "New fuzzy
wavelet neural networks for system identification and control," Applied
Soft Computing, vol. 6 ,pp. 1-17, 2005.
[4] Y. Shirvany, M. Hayati and R. Moradian, " Multilayer perceptron neural
networks with novel unsupervised training method for numerical
solution of the partial differential equations," Applied Soft Computing,
vol. 9, pp. 20-29, 2009.
[5] Z. Zainuddin and Evans, "Human face recognition using accelerated
multilayer perceptrons," International Journal of Computer
Mathematics, vol. 80, pp. 535-558, 2001.
[6] Z. Zainuddin and P. Ong, "Function approximation using artificial
neural networks," International Journal of Systems Applications,
Engineering & Development, vol. 1, pp. 173-178, 2007.
[7] A. Banakar and M. Fazle Azeem, "Artificial wavelet neural network and
its application in neuro-fuzzy models," Applied Soft Computing, vol. 8,
pp. 1463-1485, 2008.
[8] B. Biswal, P.K. Dash, B.K. Panigrahi and J.B.V. Reddy, "Power signal
classification using dynamic wavelet network," Applied Soft Computing,
vol. 9, pp. 118-125, 2009.
[9] C.J. Lin, "Nonlinear systems control using self-constructing wavelet
networks," Applied Soft Computing, vol. 9, pp. 71-79, 2009.
[10] W. Maoan, J. Shijiu, W. Likun and Z. Van, "Defect characteristic
prediction of pipeline by means of wavelet neural network based on the
hierarchical clustering algorithm (Published Conference Proceedings
style)," Proceedings of Biennial International Pipeline Conference,
Alberta, 2004, pp. 921-924.
[11] Z. Xiao-Guang, K. Ying-Zhi, G. Dao-Hua and W. Xing Biao, "Fuzzy
wavelet neural networks based on SVM," Journal of East China
University of Science and Technology, vol. 32, no. 11, pp. 1351-1354,
2006.
[12] K. Seong-Ju, K. Yong-Taek, S. Jae-Yong and J. Hong-Tae, "Design of
the scaling-wavelet neural network using genetic algorithm (Published
Conference Proceedings style)," Proceedings of the International Joint
Conference on Neural Networks, Honolulu , 2002, pp. 2174-2179.
[13] Y. Oussar and G. Dreyfus, "Initialization by selection for wavelet
network training," Neurocomputing, vol. 34, pp. 131-143, 2000.
[14] Z. Dahai, B. Yanqiu, Y.B. Bi and Y. Sun, "Design and initialization
algorithm based on modulus maxima of wavelet transform for wavelet
neural network (Published Conference Proceedings style)," International
Conference on Power System Technology, Singapore, 2004, pp. 897-
901.
[15] Q. Zhang and A. Beveniste, "Wavelet networks," IEEE Transactions on
Neural Networks, vol. 3, pp. 889-898, 1992.
[16] C. Jiacong and L. Xingchun, "Application of the diagonal recurrent
wavelet neural network to solar irradiation forecast assisted with fuzzy
technique," Engineering Applications of Artificial Intelligence, vol. 21,
pp. 1255-1263, 2008.
[17] E. Avci and D. Avci, "The performance comparison of discrete wavelet
neural network and discrete wavelet adaptive network based fuzzy
inference system for digital modulation recognition," Expert Systems
with Application, vol. 35, pp. 90-101, 2008.
[18] L. Jie, L. Dai-fei, D. Xue-ru, Z. Zhong and D. Feng-qi, "Prediction of
AI(OH)3 fluidized roasting temperature based on wavelet neural
network," Transactions of Nonferrous Metals Society of China, vol. 17,
pp. 1052-1056, 2007.
[19] K. Vinay Kumar, V. Ravi, Mahil Carr and N. Raj Kiran, "Software
development cost estimation using wavelet neural networks," The
Journal of Systems and Software, vol. 81, pp. 1853-1867, 2008.
[20] K. Hammouda, "A comparative study of data clustering techniques,"
unpublished
[21] S. Mu-Chun and C. Chien-Hsing, "A modified version of the K-means
algorithm with a distance based on cluster symmetry", IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol.23, pp.
674-680, 2001.
[22] S. Mu-Chun, C. Chen-Hsing and H. Chen-Chiung, "Fuzzy C-means
algorithm with a point symmetry distance," International Journal of
Fuzzy Systems, vol. 7, no. 4, pp. 175-181, 2005.
[23] C. Kuo-Liang and L. Jhin-Sian, "Faster and more robust point
symmetry-based K-means algprithm," Pattern Recognition, vol. 40, pp.
410-422, 2007.
[24] D. Amaratunga and J. Cabrera, Exploration and analysis of DNA
microarray and protein array data. John Wiley & Sons: New Jersey, pp.
123-126, 2004.
[25] T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P.
Mesirov, H. Coller, M.L. Loh, J.R. Downing, M.A. Caligiuri, C.D.
Bloomfield and E.S. Lander, "Molecular classification of cancer: Class
discovery and class prediction by gene expression monitoring," Science,
vol. 286, pp. 531-537, 1999.
[26] J. Khan, S.J. Wei, M. Ringnér, L.H. Saal, M. Ladanyi, F. Westermann,
F. Berthold, M. Schwab, C.R. Antonescu, C. Peterson and P. Meltzer,
"Classification and diagnostic prediction of cancers using gene
expression profiling and artificial neural network," Nature Med, vol. 7,
pp. 673-679, 2001.
[27] C.L. Nutt, D.R. Mani, R.A. Betensky, P. Tamayo, J.G. Cairncross, C.
Ladd, U. Pohl, C. Hartmann, M.E. McLaughlin, T.T. Batchelor, P.M.
Black, A.V. Deimling, A.L. Pomeroy, T.R. Golub and D.N. Louis,
"Gene expression-based classification of malignant gliomas correlates
better with survival than histological classification," Cancer Res., vol.
63, pp. 1602-1607, 2003.
[28] S.L. Pomeroy, P. Tamayo, M. Gaasenbeek, L.M. Sturla, M. Angelo,
M.E. McLaughlin, J.Y. Kim, L.C. GoumnerovaC, P.M. Black, C. Lau,
J.C. Allen, D. Zagzag, J.M. Olson, T. Curran, C. Wetmore, J.A. Biegel,
T. Poggio, S.E.S. Lander and T.R. Golub, "Prediction of central nervous
system embryonal tumor outcome based on gene expression," Nature,
vol. 415, pp. 436-442, 2002.
@article{"International Journal of Information, Control and Computer Sciences:58237", author = "Zarita Zainuddin and Ong Pauline", title = "Improved Wavelet Neural Networks for Early Cancer Diagnosis Using Clustering Algorithms", abstract = "Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K-means (KM), Fuzzy C-means (FCM), symmetry-based K-means (SBKM), symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-based K-means (MPKM) clustering algorithms in choosing the translation parameter of a WNN. These modified WNNs are further applied to the heterogeneous cancer classification using benchmark microarray data and were compared against the conventional WNN with random initialization method. Experimental results showed that a WNN classifier with the MPKM algorithm is more precise than the conventional WNN as well as the WNNs with other clustering algorithms.
", keywords = "Clustering, microarray, symmetry, wavelet neural networks.", volume = "3", number = "11", pages = "2643-7", }
