**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**31100

##### Improved Wavelet Neural Networks for Early Cancer Diagnosis Using Clustering Algorithms

**Authors:**
Zarita Zainuddin,
Ong Pauline

**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,
Symmetry,
Microarray,
wavelet neural networks

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1072255

**References:**

[1] E. G├│mez-Ram├¡rez, 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.