Comparison of SVM-based Feature Selection Method for Biological Omics Dataset

Xiao Gao

2022

Abstract

With the development of omics technology, more and more data will be generated in cancer research. Machine learning methods have become the main method of analysing these data. Omics data have the characteristics of the large number of features and small samples, but features are redundant to some extent for analysis. We can use the feature selection method to remove these redundant features. In this paper, we compare two SVM-based feature selection methods to complete the task of feature selection. We evaluate the performance of these two methods on three omics datasets, and the results showed that the SVM-RFE method performed better than the pure SVM method on these cancer datasets.

Paper Citation

in Harvard Style

Gao X. (2022). Comparison of SVM-based Feature Selection Method for Biological Omics Dataset. In Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB, ISBN 978-989-758-595-1, pages 595-600. DOI: 10.5220/0011247400003443

in Bibtex Style

@conference{icbeb22,
author={Xiao Gao},
title={Comparison of SVM-based Feature Selection Method for Biological Omics Dataset},
booktitle={Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB,},
year={2022},
pages={595-600},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011247400003443},
isbn={978-989-758-595-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB,
TI - Comparison of SVM-based Feature Selection Method for Biological Omics Dataset
SN - 978-989-758-595-1
AU - Gao X.
PY - 2022
SP - 595
EP - 600
DO - 10.5220/0011247400003443