Biological Data Classification via Faster MAXimum Feasible Subsystem Algorithm
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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".