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Feature Selection Method Based on Partial Least Squares and Analysis of Traditional Chinese Medicine Data
Joint Authors
Du, Jianqiang
Huang, Canyi
Nie, Bin
Yu, Riyue
Xiong, Wangping
Zeng, Qingxia
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-01
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The partial least squares method has many advantages in multivariable linear regression, but it does not include the function of feature selection.
This method cannot screen for the best feature subset (referred to in this study as the “Gold Standard”) or optimize the model, although contrarily using the L1 norm can achieve the sparse representation of parameters, leading to feature selection.
In this study, a feature selection method based on partial least squares is proposed.
In the new method, exploiting partial least squares allows extraction of the latent variables required for performing multivariable linear regression, and this method applies the L1 regular term constraint to the sum of the absolute values of the regression coefficients.
This technique is then combined with the coordinate descent method to perform multiple iterations to select a better feature subset.
Analyzing traditional Chinese medicine data and University of California, Irvine (UCI), datasets with the model, the experimental results show that the feature selection method based on partial least squares exhibits preferable adaptability for traditional Chinese medicine data and UCI datasets.
American Psychological Association (APA)
Huang, Canyi& Du, Jianqiang& Nie, Bin& Yu, Riyue& Xiong, Wangping& Zeng, Qingxia. 2019. Feature Selection Method Based on Partial Least Squares and Analysis of Traditional Chinese Medicine Data. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1130783
Modern Language Association (MLA)
Huang, Canyi…[et al.]. Feature Selection Method Based on Partial Least Squares and Analysis of Traditional Chinese Medicine Data. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1130783
American Medical Association (AMA)
Huang, Canyi& Du, Jianqiang& Nie, Bin& Yu, Riyue& Xiong, Wangping& Zeng, Qingxia. Feature Selection Method Based on Partial Least Squares and Analysis of Traditional Chinese Medicine Data. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1130783
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130783