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An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 2, p e0171122 (2017)
- Publication Year :
- 2017
- Publisher :
- Public Library of Science, 2017.
-
Abstract
- Using the spectrum data for quality prediction always suffers from noise and colinearity, so variable selection method plays an important role to deal with spectrum data. An efficient elastic net with regression coefficients method (Enet-BETA) is proposed to select the significant variables of the spectrum data in this paper. The proposed Enet-BETA method can not only select important variables to make the quality easy to interpret, but also can improve the stability and feasibility of the built model. Enet-BETA method is not prone to overfitting because of the reduction of redundant variables realized by elastic net method. Hypothesis testing is used to further simplify the model and provide a better insight into the nature of process. The experimental results prove that the proposed Enet-BETA method outperforms the other methods in terms of prediction performance and model interpretation.
- Subjects :
- 0301 basic medicine
Computer science
lcsh:Medicine
Overfitting
01 natural sciences
010104 statistics & probability
Spectrum Analysis Techniques
Chemical Precipitation
lcsh:Science
Triticum
Plant Proteins
Principal Component Analysis
Multidisciplinary
Crystallography
Data Processing
Spectroscopy, Near-Infrared
Covariance
Physics
Applied Mathematics
Simulation and Modeling
Chemical Reactions
near-Infrared Spectroscopy
Absorption Spectroscopy
Agriculture
Plants
Condensed Matter Physics
Chemistry
Principal component analysis
Wheat
Physical Sciences
Regression Analysis
Crystallization
Information Technology
Algorithm
Algorithms
Research Article
Elastic net regularization
Computer and Information Sciences
Feature selection
Infrared Spectroscopy
Crops
Research and Analysis Methods
Stability (probability)
03 medical and health sciences
Linear regression
Solid State Physics
Grasses
0101 mathematics
Segmented regression
Least-Squares Analysis
Statistical hypothesis testing
Genetic Algorithms
Spectrum Analysis
lcsh:R
Organisms
Biology and Life Sciences
Random Variables
Bayes Theorem
Probability Theory
Computing Methods
Noise
030104 developmental biology
Multivariate Analysis
lcsh:Q
Mathematics
Crop Science
Cereal Crops
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....3710845dc6863b341537343aff7a5880