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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Different strategies for class model optimization. A comparative study.

Zuzanna Małyjurek1, Raffaele Vitale2, Beata Walczak1

  • 1Institute of Chemistry, University of Silesia, Szkolna 9, 40-006, Katowice, Poland.

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|April 22, 2020
PubMed
Summary
This summary is machine-generated.

Class Modelling (CM) parameter optimization for Soft Independent Modelling of Class Analogy (SIMCA) models was compared. Rigorous optimization, using only target class samples, is a safer choice for tuning SIMCA models, especially when nontarget data may be unrepresentative.

Keywords:
Compliant approachModel optimizationRigorous approachSIMCAValidation

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Area of Science:

  • Chemometrics
  • Analytical Chemistry
  • Machine Learning

Background:

  • Class Modelling (CM) develops mathematical models to classify new samples.
  • Soft Independent Modelling of Class Analogy (SIMCA) is a key CM approach used in quality control and authenticity verification.
  • Optimizing CM parameters is critical for model performance.

Purpose of the Study:

  • To compare resampling methods for SIMCA model optimization.
  • To evaluate compliant versus rigorous optimization strategies.
  • To assess different decision threshold estimation criteria.

Main Methods:

  • Four resampling methods were tested for SIMCA optimization.
  • Optimization was performed using both compliant (target and nontarget data) and rigorous (target data only) strategies.
  • Decision thresholds were either fixed a priori or data-driven optimized.
  • Real-world datasets and ANalysis Of VAriance (ANOVA) were used for assessment.

Main Results:

  • Both compliant (optimized threshold) and rigorous (fixed threshold) approaches yielded satisfactory classification.
  • Unrepresentative nontarget data can bias compliant optimization.
  • Rigorous optimization demonstrated robustness against biased data.

Conclusions:

  • Rigorous optimization is a safer strategy for tuning SIMCA models.
  • Careful consideration of nontarget data representativeness is crucial for compliant optimization.