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Self-Optimized One-Class Classification Using Sum of Ranking Differences Combined with a Receiver Operator

Tony Lemos1, John H Kalivas1

  • 1Department of Chemistry, Idaho State University, Pocatello, Idaho 83209, United States.

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This study introduces a hybrid fusion method for one-class classification, combining multiple non-optimized classifiers to reliably authenticate samples. The approach enhances accuracy, sensitivity, and specificity in analytical chemistry tasks.

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Spectroscopy

Background:

  • One-class classification (class modeling) is crucial for tasks like food authentication and medical diagnosis.
  • Selecting and optimizing one-class classifiers, alongside choosing appropriate spectroscopic data preprocessing and spectral regions, presents significant challenges.
  • Existing methods often require extensive optimization for each classifier and dataset.

Purpose of the Study:

  • To develop a hybrid fusion process capable of combining multiple, non-optimized one-class classifiers.
  • To address the challenges of classifier selection, parameter tuning, and data preprocessing in spectroscopic analysis.
  • To demonstrate a flexible and reliable approach for class modeling using spectroscopic data.

Main Methods:

  • A hybrid fusion process combining non-optimized classifiers across multiple instruments, preprocessing methods, and measurements.
  • Utilizing a window of tuning parameters for each classifier instead of full optimization.
  • Applying the sum of ranking differences (SRD) method for flexible fusion of assessment values.
  • Automatically optimizing the SRD ranking value (threshold) using a receiver operator characteristic (ROC) curve.

Main Results:

  • The hybrid fusion approach demonstrated reliable classification performance on two distinct analytical datasets.
  • The first dataset involved beer authentication using five instruments (NIR, MIR, UV-Vis, TGA), with three fusion protocols tested.
  • The second dataset analyzed MIR spectra of strawberry puree for authenticity, achieving reliable accuracy, sensitivity, and specificity.

Conclusions:

  • Fusing non-optimized classifiers offers a reliable strategy for one-class classification in analytical chemistry.
  • The proposed hybrid fusion method, utilizing SRD and ROC curve optimization, effectively handles multi-instrument spectroscopic data.
  • This approach provides a robust solution for class modeling problems, improving classification reliability without extensive individual classifier optimization.