Improved Wavelet Neural Networks for Early Cancer Diagnosis Using Clustering Algorithms
| Main Authors: | Zarita Zainuddin, Ong Pauline |
|---|---|
| Format: | Article |
| Bahasa: | eng |
| Terbitan: |
, 2009
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/1072255 |
Daftar Isi:
- 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.