Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network
Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, two different approaches were proposed for improving the predictive capability of WNNs. First, the types of activation functions used in the hidden layer of the WNN were...
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| Format: | Article |
| Language: | en |
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Elsevier
2011
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| Online Access: | http://eprints.uthm.edu.my/4230/1/AJ%202017%20%28584%29.pdf http://eprints.uthm.edu.my/4230/ https://dx.doi.org/10.1016/j.eswa.2011.04.164 |
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| 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 | Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, two different approaches were proposed for improving the predictive capability of WNNs. First, the types of activation functions used in the hidden layer of the WNN were varied. Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MSFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. The modified WNN was then applied to heterogeneous cancer classification using four different microarray benchmark datasets. The comparative experimental results showed that the proposed methodology achieved an almost 100% classification accuracy in multiclass cancer prediction, leading to superior performance with respect to other clustering algorithms. Subsequently, performance comparisons with other classifiers were made. An assessment analysis showed that this proposed approach outperformed most of the other classifiers. |
| format | Article |
| id | my.uthm.eprints-4230 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2011 |
| publisher | Elsevier |
| record_format | eprints |
| spelling | my.uthm.eprints-42302021-12-01T07:01:08Z http://eprints.uthm.edu.my/4230/ Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network Zainuddin, Zarita Pauline, Ong TK7800-8360 Electronics Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, two different approaches were proposed for improving the predictive capability of WNNs. First, the types of activation functions used in the hidden layer of the WNN were varied. Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MSFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. The modified WNN was then applied to heterogeneous cancer classification using four different microarray benchmark datasets. The comparative experimental results showed that the proposed methodology achieved an almost 100% classification accuracy in multiclass cancer prediction, leading to superior performance with respect to other clustering algorithms. Subsequently, performance comparisons with other classifiers were made. An assessment analysis showed that this proposed approach outperformed most of the other classifiers. Elsevier 2011 Article PeerReviewed text en http://eprints.uthm.edu.my/4230/1/AJ%202017%20%28584%29.pdf Zainuddin, Zarita and Pauline, Ong (2011) Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network. Expert Systems with Applications, 38 (11). pp. 13711-13722. ISSN 0957-4174 https://dx.doi.org/10.1016/j.eswa.2011.04.164 |
| spellingShingle | TK7800-8360 Electronics Zainuddin, Zarita Pauline, Ong Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network |
| title | Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network |
| title_full | Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network |
| title_fullStr | Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network |
| title_full_unstemmed | Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network |
| title_short | Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network |
| title_sort | reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network |
| topic | TK7800-8360 Electronics |
| url | http://eprints.uthm.edu.my/4230/1/AJ%202017%20%28584%29.pdf http://eprints.uthm.edu.my/4230/ https://dx.doi.org/10.1016/j.eswa.2011.04.164 |
| url_provider | http://eprints.uthm.edu.my/ |