Outlier Identification Method Based on Multi-Model Weighted Consensus in Conjunction With Monte Carlo Cross-Validation
A multi-model dynamic weighted consensus outlier identification for near-infrared spectroscopy (NIRS) data is proposed. This dynamic weighting method effectively addresses the biases that can occur with simple averaging. The data after removing outliers using consensus methods is more suitable for modeling with a wider range of models.
Authors
- Yujing Wang (ORCID: https://orcid.org/0000-0002-9452-4555)
- Zhengguang Chen (ORCID: https://orcid.org/0000-0002-6791-4240)
- Jinming Liu (ORCID: https://orcid.org/0000-0001-8328-873X)
- He Wang (ORCID: https://orcid.org/0000-0002-2937-0529)
Institutions
- Heilongjiang Bayi Agricultural University (CN)
Publication Details
- Journal
- Journal of AOAC International
- Published
- 2025-06-21
- DOI
- https://doi.org/10.1093/jaoacint/qsaf061
- Citations
- 3
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 2.26