Validation-based Decision Making in Data-driven Evolutionary Computation: A Case Study in Multi-objective Feature Selection
Why this work is in the frame
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Bibliographic record
Abstract
Overfitting occurs when a model captures noise and incorrect patterns, reducing its accuracy. This challenge extends to data-driven evolutionary optimization, such as multi-objective feature selection, where relying on training data for decision-making can degrade test set performance. Inspired by machine learning practices, this paper proposes using validation data for decision-making to reduce overfitting in multi-objective feature selection, focusing on classification error rate and the number of selected features as objectives. Although algorithm-independent, the framework is demonstrated using established multi-objective optimization algorithms. New metrics compare Pareto fronts from training- and validation-based approaches. Experiments on fourteen datasets reveal that validation-based decision-making significantly outperforms training-based methods, particularly by reducing the number of selected features while maintaining effectiveness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it