Hybrid GTO-SA Metaheuristic for Feature Subset Selection in High-Dimensional Medical Datasets
Bibliographic record
Abstract
Feature subset selection is a crucial preprocessing step for improving classification performance and reducing model complexity, particularly in high-dimensional medical datasets.This paper proposes GTO-SA, a hybrid metaheuristic that integrates the global exploration ability of the Gorilla Troops Optimizer (GTO) with the local exploitation strength of Simulated Annealing (SA).By embedding SA into the GTO framework, the method achieves a more effective exploration-exploitation balance, mitigating premature convergence and improving feature subset optimization.GTO-SA was evaluated on sixteen benchmark medical datasets from the UCI and Kaggle repositories.Experimental results show that the proposed approach achieves an average classification accuracy improvement of 3-7% compared to baseline algorithms and reduces the number of selected features by over 50% on average, while maintaining convergence stability.Compared with Gorilla Troops Optimizer, Particle Swarm Optimization, Ant Lion Optimization, and the Sine Cosine Algorithm, GTO-SA consistently delivers superior accuracy, compact feature subsets, and faster convergence.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 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".