An effective and novel wavelet neural network approach in classifying type 2 diabetics
Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for t...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Czech Technical University
2012
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4216/1/AJ%202017%20%28581%29.pdf http://eprints.uthm.edu.my/4216/ https://dx.doi.org/10.14311/NNW.2012.22.025 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833417541210341376 |
|---|---|
| author | Zainuddin, Zarita Pauline, Ong |
| author_facet | Zainuddin, Zarita Pauline, Ong |
| author_sort | Zainuddin, Zarita |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm – specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm – in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved. |
| format | Article |
| id | my.uthm.eprints-4216 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2012 |
| publisher | Czech Technical University |
| record_format | eprints |
| spelling | my.uthm.eprints-42162021-12-01T05:37:46Z http://eprints.uthm.edu.my/4216/ An effective and novel wavelet neural network approach in classifying type 2 diabetics Zainuddin, Zarita Pauline, Ong TK7800-8360 Electronics Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm – specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm – in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved. Czech Technical University 2012 Article PeerReviewed text en http://eprints.uthm.edu.my/4216/1/AJ%202017%20%28581%29.pdf Zainuddin, Zarita and Pauline, Ong (2012) An effective and novel wavelet neural network approach in classifying type 2 diabetics. Neural Network World, 22 (5). pp. 407-428. ISSN 1210-0552 https://dx.doi.org/10.14311/NNW.2012.22.025 |
| spellingShingle | TK7800-8360 Electronics Zainuddin, Zarita Pauline, Ong An effective and novel wavelet neural network approach in classifying type 2 diabetics |
| title | An effective and novel wavelet neural network approach in classifying type 2 diabetics |
| title_full | An effective and novel wavelet neural network approach in classifying type 2 diabetics |
| title_fullStr | An effective and novel wavelet neural network approach in classifying type 2 diabetics |
| title_full_unstemmed | An effective and novel wavelet neural network approach in classifying type 2 diabetics |
| title_short | An effective and novel wavelet neural network approach in classifying type 2 diabetics |
| title_sort | effective and novel wavelet neural network approach in classifying type 2 diabetics |
| topic | TK7800-8360 Electronics |
| url | http://eprints.uthm.edu.my/4216/1/AJ%202017%20%28581%29.pdf http://eprints.uthm.edu.my/4216/ https://dx.doi.org/10.14311/NNW.2012.22.025 |
| url_provider | http://eprints.uthm.edu.my/ |