Abstract
Variable selection is one of the important practical issues for many scientific areas, particularly in chemometrics, where data sets include several hundreds of variables and low number of observations. The aim of this paper is to compare some newly proposed variable selection methods by means of extensive simulation studies and to give some practical hints for use of the compared methods. Furthermore, we underpin the performances of compared methods based on real data examples.
Keywords
Chemometrics
Feature selection
Computer science
Variable (mathematics)
Selection (genetic algorithm)
Regression
Regression analysis
Data mining
Machine learning
Artificial intelligence
Statistics
Mathematics
Subject Areas
Advanced Statistical Methods and Models
·Statistics and Probability ·Physical Sciences
Spectroscopy and Chemometric Analyses
·Analytical Chemistry ·Physical Sciences
Statistical Methods and Inference
·Statistics and Probability ·Physical Sciences
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