Zarita Zainuddin and Ong Pauline
Improved Wavelet Neural Networks for Early Cancer Diagnosis Using Clustering Algorithms
2550 - 2556
2009
3
11
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/9166
https://publications.waset.org/vol/35
World Academy of Science, Engineering and Technology
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, Kmeans (KM), Fuzzy Cmeans (FCM), symmetrybased Kmeans (SBKM), symmetrybased Fuzzy Cmeans (SBFCM) and modified point symmetrybased Kmeans (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.
Open Science Index 35, 2009