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Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network

Zainuddin, Zarita and Ong, Pauline (2011) Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network. Expert Systems with Applications, 38. pp. 13711-13722. ISSN 09574174

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Bioinformatics; conditional t-test; fuzzy c-means clustering; microarray; wavelet neural network
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Engineering Mechanics
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 20 Feb 2019 08:13
Last Modified: 20 Feb 2019 08:13
URI: http://eprints.uthm.edu.my/id/eprint/10762
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