An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data

@article{Zhang2018AnEF,
  title={An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data},
  author={Ying Zhang and Qingchun Deng and Wenbin Liang and Xianchun Zou},
  journal={BioMed Research International},
  year={2018},
  volume={2018}
}
The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions. In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter… 
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