Wang Shitong
School of Information Engineering, Southern Yangtze University, Wuxi, China
F. L. Chung
Department of Computing, Hong Kong Polytechnic University, Hong Kong, China
Deng Zhaohong
School of Information Engineering, Southern Yangtze University, Wuxi, China
L. I.N. Qing
Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
H. U. Dewen
School of Automation, National Defense University of Science and Technology, Changsha, China
ABSTRACT
In this study, based on DCCM (Differential Capability Control Machine), the new feature-extraction criterion NFEC is developed using the first-order differential information and a new feature-extraction algorithm DCCFE is accordingly proposed for binary classification problems. NFEC and DCCFE are then extended to their multi-classification versions, i.e., m_NFEC and m_DCCFE, respectively. Present experimental results demonstrate that the new feature extraction criteria and algorithms outperform or have comparable performance with the current methods for cancer gene expression datasets. Furthermore, since the new algorithms here admit more general first-order differential functions as the basis functions instead of kernel functions in SVM-based method, they perhaps have more potential applications in bioinformatics in the future.
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How to cite this article
Wang Shitong, F. L. Chung, Deng Zhaohong, L. I.N. Qing and H. U. Dewen, 2005. New Feature-extraction Criteria and Classification Algorithms for Cancer Gene Expression Datasets. Biotechnology, 4: 163-172.
DOI: 10.3923/biotech.2005.163.172
URL: https://scialert.net/abstract/?doi=biotech.2005.163.172
DOI: 10.3923/biotech.2005.163.172
URL: https://scialert.net/abstract/?doi=biotech.2005.163.172
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