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Molecules 2017, 22(12), 2086;

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony

School of Medical Information, Wannan Medical College, Wuhu 241002, China
School of Mathematics and Computer Science, Anhui Normal University, Wuhu 241002, China
Author to whom correspondence should be addressed.
Received: 27 October 2017 / Accepted: 23 November 2017 / Published: 29 November 2017
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Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification. View Full-Text
Keywords: intelligent optimization; cancer classification; PSO; ABC; SVM intelligent optimization; cancer classification; PSO; ABC; SVM

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Gao, L.; Ye, M.; Wu, C. Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony. Molecules 2017, 22, 2086.

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