A novel multi-label deep forest algorithm based on elite preservation strategy and multi-layer feature fusion
Why this work is in the frame
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Bibliographic record
Abstract
Multi-label classification is a popular research direction in the field of machine learning and pattern recognition, and has shown significant application potential in real-life scenarios. However, traditional multi-label classification algorithms still suffer from the low classification accuracy and instability due to the lack of feature diversity. To tackle this issue, this paper investigates the effect of feature diversity on the ensemble model based on the deep forest framework, and subsequently presents a multi-label deep forest algorithm based on elite preservation strategy and multi-layer feature fusion (EMDF). Firstly, the elite preservation strategy is introduced to screen the forests in the cascades layer by layer for reestablishing the cascades structure. This enables the screened cascades to acquire better predicted feature vectors. Secondly, combined with the predicted feature vectors of screened cascades, the multi-layer feature fusion strategy is put forward to enhance the label information of input features and improve the predictive performance of the model. Finally, EMDF is extensively tested on 10 different types of comparative algorithms and 12 various fields datasets. Experimental results show that EMDF achieves better accuracy on multiple metrics such as Hamming loss and Coverage on most datasets. Furthermore, a comprehensive evaluation of algorithm rankings on all metrics also demonstrates the superior stability of EMDF.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| 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