A Novel Multi-objective Binary Differential Evolution Algorithm for Multi-label Feature Selection
Why this work is in the frame
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Bibliographic record
Abstract
In machine learning, multi-label classification aims to assign labels of instances in a dataset which are associated to more than one class label. Feature selection as an important task in predictive model construction improves the performance of multi-label classification. Since feature selection task can be interpreted as optimizing multiple objectives in a massive search space, multi-objective evolutionary techniques can be applied to tackle this family of problems. In this paper, a binary multi-objective feature selection is proposed for multi-label data with considering number of features and classification accuracy as objectives. A binary differential evolution is proposed based on opposition-based learning concept and partially voting between two candidate solutions to decide about absence or presence of a feature in third randomly selected solution. Because feature selection is basically a binary optimization problem, proposing a binary operator improves the effectiveness of search process in evolutionary algorithms. The proposed operator is utilized in third version of Generalized Differential Evolution (GDE3) which is a multi-objective optimization algorithm to select best subset of multi-label features with minimum number of features. A benchmarking is conducted on eight multi-label datasets in terms of several multi-objective assessment metrics including the Hypervolume indicator, Pure Diversity, and Set-coverage. Experimental results show significant improvements for proposed method in comparison with the state-of-the-art multi-objective feature selection methods for multi-label classification, which are namely NSGA-II and PSO based approaches.
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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 | 0.001 |
| 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