Adaptive Learning with Logistic Regression for soil classification with LIBS
Bibliographic record
Abstract
Laser-induced breakdown spectroscopy (LIBS) presents a promising avenue for real-time soil characterization, particularly with the integration of machine learning (ML) techniques to enhance its detection capabilities. However, the variability in soil matrix properties can lead to discrepancies in LIBS emission lines, challenging traditional ML algorithms that assume consistent distributions between training and test spectra. To address this challenge, we propose a novel domain adaptation approach leveraging logistic regression with class and pseudo-label weighting. We categorize classes based on transfer difficulties into hard, normal, and easy categories. Hard classes, with low predictive proportions due to misclassification, are assigned higher weights. In contrast, lower weights are assigned to easy classes to mitigate transfer imbalances. Furthermore, we introduce weighted pseudo-labels from hard classes misclassified into similar classes, obtained through few-shot learning based on similarity, to be incorporated into co-training alongside source-labeled samples. It refines the logistic regression model and boosts the test performance of hard classes. To highlight the effectiveness of the proposed method, it is tested with the Euro-Mediterranean Symposium on LIBS (EMSLIBS) contest dataset and compared against literature methods, including data calibration, adversarial training, and self-learning. Notably, our method outperforms these methods, achieving an impressive accuracy of 91.3%.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".