Domain Adaptation Using Class-Balanced Self-Paced Learning for Soil Classification With LIBS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Laser-induced breakdown spectroscopy (LIBS) is a promising technology for soil analysis due to its simple setup, cost-effectiveness, and rapid (few seconds) analysis time per sample. The recent rise in machine learning (ML) techniques for processing LIBS spectra has made LIBS more attractive. However, because of the soil samples’ varied physical properties and chemical composition, the emission lines’ distribution can be highly variable. It may cause spectra distribution change and make the training spectra not representative of the test spectra. Hence, applying an ML model trained with only samples from the training distribution to the test distribution will likely experience performance degradation. To solve the spectra distribution problem, we propose using self-learning, a domain adaptation (DA) method to self-adapt to the domain shift. It involves an iterative process of predicting on the target domain with the model trained by the source domain and then taking the confident predictions as pseudolabels for co-training the model. On top of self-learning, we also propose a novel class-balanced self-paced learning method. It balances the classes in the co-training process by ignoring the easy classes, which has a large predictive proportion to avoid the gradual dominance of these classes in pseudolabel generation. Instead of using universal selection proportion and in addition to achieve various confidence thresholds for classes, the proposed method balances and self-paces the other classes by customizing the class selection proportion and increment to avoid model bias in the self-training process. The class selection proportion and addition are tuned by validation, in which the validation set is generated by decision fusion of convolutional neural networks and partial least-squares discriminant analysis. Our method is tested with the Euro-Mediterranean symposium on LIBS (EMSLIBS) dataset, which shows the proposed method can effectively handle the spectra distribution change and achieves 90.2% test accuracy. It is comparable to the EMSLIBS contest winners with the same dataset, which uses test data calibration.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it