Genetic support vector machines as powerful tools for the analysis of biomedical Raman spectra
Bibliographic record
Abstract
Abstract The growing number of applications of Raman spectroscopy in medicine necessitates the development of robust and accurate processing methods. The two major tasks for which Raman spectra are used are quantifying chemical species in a sample (regression) and discriminating chemically distinct samples (classification). Conventionally, linear techniques, primarily projection to latent structures (PLS), are used to perform these tasks. However, there are a number of nonlinearities that may arise when acquiring the Raman spectra of biomedical samples, such as scattering differences between tissues or autofluorescence variances, which makes nonlinear methods more suitable. To this end, we compared kernelized support vector machines (SVM) to PLS for a number of biomedical Raman datasets. Additionally, this work develops a genetic SVM, wherein the parameters of a SVM are selected by a classical genetic algorithm instead of the conventional grid search. This facilitates the use of complex kernels, which yield higher performance than simple kernel functions. We have found that this genetic SVM outperforms PLS in all of the regression tasks examined in this paper, while yielding equivalent results for classification tasks. Additionally, we have found that the genetic algorithm provides significant time savings in the optimization of the SVM parameters over grid search.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".