A novel multi-label deep forest algorithm based on elite preservation strategy and multi-layer feature fusion
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".