Enhanced Artificial Bee Colony Algorithm with Pretrained Model Functional Weight and Modified Selection Strategy for Text Classification
Bibliographic record
Abstract
Previous works have proposed various techniques to address the premature convergence problem, where candidate solutions get trapped in local optima instead of reaching the global optimum.This has been tackled using different selection methods in metaheuristic search algorithms.However, while much of the literature focuses on either the search operators or the creation of algorithm variants, research indicates that the effectiveness of the search procedure depends on both the search operators and the selection methods.Incorporating problem-specific functional weights enhances dynamic adaptation to data patterns, reflects data relevance, and improves generalization.This paper offers an enhanced Artificial Bee Colony algorithm including functional weights and a modified selection strategy (ABC-FWMSS) to prioritize features, aiming to achieve an optimal solution and a dynamic balance between exploration and exploitation.The exploration ability of the Artificial Bee Colony is enhanced using pretrained model functional weights during the employed bee phase, while its exploitative capabilities are boosted using tournament selection and employed bee index during the onlooker bee phase.This approach dynamically balances exploration and exploitation.The proposed method achieved 96% precision on the 20 Newsgroups dataset, with the highest fitness score and a 48.8% drop in the number of selected features.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".