Binary Hybrid Differential Evolution Algorithm for Multi-label Feature Selection
Why this work is in the frame
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Bibliographic record
Abstract
Driven by the recent technological advancements within the machine learning field, multi-label classification has been introduced as one of the challenging tasks to assign more than one label to each instance in a dataset. Feature selection is one of the predominant feature engineering methodologies which being extensively used as a vital step in predictive model construction to enhance the multi-label classification performance. Many metaheuristic algorithms have been tailored to choose the optimal subset of features in datasets but as a challenging problem, such algorithms suffer from a slow process during fine-tuning. Objective of this paper is to propose a hybrid mechanism by which an obtained feature subset from a Binary Differential Evolution (BDE) algorithm will be further enhanced to minimize the classification error using a local search methodology. Key motivation behind the proposed model is to address the weakness in exploitation of metaheuristic feature selection algorithms with the help of classical feature selection method such as Sequential Backward Selection (SBS) as a local search strategy. The classical feature selection method eliminates more redundant and irrelevant features of obtained subset using the BDE to decrease the classification error. The empirical results obtained on eight various multi-label datasets show that the proposed hybrid approach, which is a fusion of both evolutionary and classical feature selection methods, can minimize the classification error on the obtained feature subset using the BDE.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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