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Outlier Identification Method Based on Multi-Model Weighted Consensus in Conjunction With Monte Carlo

Yujing Wang1, Zhengguang Chen1, Jinming Liu1

  • 1Heilongjiang Bayi Agricultural University, College of Information and Electrical Engineering, Daqing 163319, China.

Journal of AOAC International
|June 28, 2025
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Summary

A new Monte Carlo cross-validation with Weighted Consensus (MCWC) method improves outlier identification for robust model development. This approach enhances predictive accuracy and reduces model dependence in spectral quantitative analysis.

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Area of Science:

  • Chemometrics
  • Data Science
  • Spectroscopy

Background:

  • Accurate outlier identification is crucial for building reliable predictive models.
  • Single-model outlier detection can lead to inadequate results, including false positives, false negatives, and model dependency.

Purpose of the Study:

  • To introduce a novel method, Monte Carlo cross-validation with Weighted Consensus (MCWC), for robust outlier identification.
  • To assess the effectiveness of MCWC in improving model performance and reducing model dependence compared to single-model methods.

Main Methods:

  • MCWC integrates Monte Carlo random sampling with multiple regression models: Partial Least Squares Regression (PLSR), Gaussian Process Regression (GPR), and Support Vector Regression (SVR).
  • Predictions from these models are combined using a dynamic weighted consensus approach for outlier detection.
  • The method was tested on a dataset of 305 sorghum samples for protein prediction using near-infrared (NIRS) spectral data.

Main Results:

  • The MCWC method demonstrated superior outlier identification compared to single-model approaches.
  • Models built on data preprocessed with MCWC showed an average R² of 0.8525.
  • In contrast, using only Monte Carlo with PLSR for outlier removal resulted in an average R² of 0.8037, indicating improved predictive performance with MCWC.

Conclusions:

  • The MCWC method offers enhanced accuracy for identifying near-infrared spectral outliers, mitigating issues like false positives, false negatives, and model dependence.
  • This approach leads to improved predictive performance in calibration models for spectral quantitative analysis.
  • The dynamic weighted consensus strategy effectively handles biases associated with simple averaging, making data more suitable for diverse modeling techniques.