Locally-weighted-RoBoost-PLS: a multivariate calibration approach to simultaneously cope with non-linearities and outliers
Bibliographic record
Abstract
BACKGROUND: Partial Least Squares regression (PLS) is a widely used tool for predictive modelling, particularly when dealing with multivariate datasets with dependent variables exhibiting strong collinearities. However, when relationships between variables are non-linear or atypical data points have to be coped with, PLS calibration models may face challenges. In recent years, different variants of the original PLS algorithm have been proposed to overcome these limitations. On the one hand, several robust regression methods that down-weigh outlying observations during the model training phase like RoBoost-PLS have been developed to reduce the detrimental effect of outliers on the performance of PLS. On the other hand, local modelling approaches, like K-Nearest-Neighbours-Locally-Weighted-PLS (KNN-LW-PLS), have been designed to handle non-linearities by fitting for each new incoming sample a separate linear calibration model considering only its nearest-neighbours. Unfortunately, none of these strategies can address the two aforementioned problems simultaneously. This paper introduces a novel approach named Locally-Weighted-RoBoost-PLS (LW-RoBoost-PLS), that combines the strengths of both local and robust modelling methodologies in order to deal with non-linearities while mitigating at the same time the influence of outliers. RESULTS: The performance of LW-RoBoost-PLS was evaluated on simulated and real industrial data (with this latter resulting from a continuous Acrylonitrile-Butadiene-Styrene ABS production process conducted at Versalis S.p.A.), both characterised by the simultaneous presence of outliers and non-linear relationships among measured variables. In the two case-studies investigated here, LW-RoBoost-PLS outperformed RoBoost-PLS and KNN-LW-PLS, achieving considerable reductions in the prediction error and prediction bias, which demonstrates that this technique permits to effectively overcome the limitations of the other approaches. SIGNIFICANCE: This paper describes a novel multivariate calibration approach named LW-RoBoost-PLS, which provides a solution for predictive modelling in scenarios where outliers and non-linearities co-exist. LW-RoBoost-PLS simultaneously handles non-linearities and outliers by combining local and robust modelling strategies, leading to improved prediction accuracy and reduced bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".