Frequency-Based Multi-Objective Feature Selection to Enhance the Generalization of Evolutionary Algorithms
Bibliographic record
Abstract
Feature selection is a critical preprocessing task in machine learning, particularly with high-dimensional datasets and decision-making while handling big data which presents significant challenges. This paper introduces an innovative approach for multi-objective feature selection, aiming to minimize the number of features and classification error simultaneously. The method mitigates the generalization issues commonly faced when relying on the results of a single run of evolutionary algorithms. Our approach leverages the frequency of each feature across multiple runs of the optimization algorithm, applied to different portions of the data, as a key metric for ranking the features. This can reduce the risk of overfitting and enhances generalization by capturing more reliable features through repeated runs and different data subsets. To enhance the robustness of the selection, we incorporate the correlation between features and labels to determine the final feature set. To evaluate the proposed method, we selected fourteen datasets with varying numbers of features and instances. Experimental results demonstrate that this post-optimization processing technique significantly enhances generalization and consistently delivers superior performance across various datasets compared to the raw optimization results.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".