Cancer-Related Gene Signature Selection Based on Boosted Regression for Multilayer Perceptron

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Gene expression profiling is a useful technique for analyzing cellular function, and gene expression profiles are widely studied in human cancer research. Gene expression data usually consist of a very large number of features and a relatively small number of samples, and extracting a small number of important features from these data is a major challenge of gene expression-based analysis in cancer research. In this paper, we propose an embedded feature selection algorithm using boosted linear regression-based feature selection. The boosting technique is applied to derive the ensemble feature selector and improve the performance of linear regression-based feature selection. The proposed feature selection algorithm, called boosted regression-based feature selection for the multilayer perceptron (BREG-MLP), repeats the boosted feature selection process to extract the smallest feature subset while maintaining good classification performance. We apply the proposed BREG-MLP to some human cancer-related gene expression data sets for the purpose of extracting important features, and we confirm that BREG-MLP offers improved performance compared to single regression-based feature selection methods.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-04
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.8, pp.64992 - 65004

ISSN
2169-3536
DOI
10.1109/ACCESS.2020.2985414
URI
http://hdl.handle.net/10203/274343
Appears in Collection
EE-Journal Papers(저널논문)
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