article · 2009

Network Fault Feature Selection Based on Adaptive Immune Clonal Selection Algorithm

Li Zhang, Xiangru Meng, Weijia Wu, Hua Zhou

Cited by 7Abstract and details only

Abstract

In order to select the most predictive features from network sample data for fault diagnosis, a novel adaptive immune clonal selection algorithm (AICSA) is proposed. By simulating the mechanisms of biological immune system such as immune memory, clone selection and self-adaptation, AICSA achieves the dynamic control of evolution process, which realizes global optimal computing combined with the local searching. Compared with traditional evolutionary algorithms, the proposed algorithm not only has higher convergence rate and better searching ability, but also can avoid prematurity and degeneration phenomenon. The experimental results show that feature selection for machine learning is necessary, and AICSA can efficiently reduce the number of features while improving the performance of network fault diagnosis based on SVM.

Cite this paper

APA 7

Zhang, L., Meng, X., Wu, W., & Zhou, H. (2009). Network Fault Feature Selection Based on Adaptive Immune Clonal Selection Algorithm. https://doi.org/10.1109/cso.2009.342

MLA 9

Zhang, Li, et al. “Network Fault Feature Selection Based on Adaptive Immune Clonal Selection Algorithm.” 2009, pp. 969–73, https://doi.org/10.1109/cso.2009.342.

Chicago

Zhang, Li, Xiangru Meng, Weijia Wu, and Hua Zhou. 2009. “Network Fault Feature Selection Based on Adaptive Immune Clonal Selection Algorithm.” https://doi.org/10.1109/cso.2009.342.

Harvard

Zhang, L. et al. (2009) ‘Network Fault Feature Selection Based on Adaptive Immune Clonal Selection Algorithm’. Available at: https://doi.org/10.1109/cso.2009.342.

BibTeX

@article{zhang2009network,
  title = {{Network Fault Feature Selection Based on Adaptive Immune Clonal Selection Algorithm}},
  author = {Zhang, Li and Meng, Xiangru and Wu, Weijia and Zhou, Hua},
  pages = {969--973},
  year = {2009},
  doi = {10.1109/cso.2009.342}
}

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