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A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate

Yong-Huan Yun1, Wei-Ting Wang1, Min-Li Tan1

  • 1College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China.

Analytica Chimica Acta
|December 21, 2013
PubMed
Summary

Iteratively Retaining Informative Variables (IRIV) effectively selects optimal variable subsets from high-dimensional datasets by identifying informative variables. This method offers a robust alternative to existing variable selection strategies.

Keywords:
Informative variablesIteratively retaining informative variablesPartial least squaresRandom combinationVariable selection

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Area of Science:

  • Chemometrics
  • Data Science
  • Machine Learning

Background:

  • High-dimensional datasets present challenges for effective variable subset selection.
  • Existing methods may not fully account for variable interactions.

Purpose of the Study:

  • To propose a novel variable selection strategy, Iteratively Retaining Informative Variables (IRIV).
  • To evaluate IRIV's performance in handling high-dimensional data and variable interactions.

Main Methods:

  • IRIV classifies variables into informative, uninformative, and interfering categories.
  • It iteratively retains strongly and weakly informative variables, removing uninformative and interfering ones.
  • IRIV was tested in conjunction with Partial Least Squares (PLS) on three datasets.

Main Results:

  • IRIV demonstrated strong performance in variable selection.
  • It proved to be a competitive alternative compared to Genetic Algorithm-PLS, MC-UVE-PLS, and CARS.
  • The method effectively handles datasets with high dimensionality.

Conclusions:

  • IRIV is a valuable and effective variable selection strategy for high-dimensional data.
  • The proposed method offers a good alternative to established techniques.
  • MATLAB source code is available for academic research.