Biological Data Classification via Faster MAXimum Feasible Subsystem Algorithm
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
Machine Learning (ML) techniques are the foundation of many next-generation technologies and have been swiftly adopted in a variety of applications in healthcare from prognosis and medical diagnosis to personalized treatment and drug manufacturing. A central model in ML is classification, an important tool for intelligent decision making in medicine such as distinguishing ill patients from a normal population. In this paper, an algorithm using improved solution methods for the MAXimum Feasible Subsystem (MAX FS) problem is applied to biological classification problems in the UCI database. The proposed method is compared with four widely used classification models using 10-fold cross-validation with and without hyperparameter tuning. The proposed method provides higher accuracy in more cases than the comparators and shows promising results for recall-oriented ML tasks since it improves the average recall by 17% when compared to other well-known classifiers such as K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it