Three-Way Decision Enhanced Graph Convolutional Networks for Text Classification
Why this work is in the frame
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Bibliographic record
Abstract
The graph convolutional network (GCN) has demonstrated effectiveness well in the text classification task. However, inadequate handling of uncertainty in prediction results exists due to the under-utilization of text features extracted by a single deep-learning model. To mitigate the potential risk of text misclassification, we proposed an enhanced GCN model for text classification based on three-way decision, incorporating shadowed set theory (3WD-GCN). In this approach, we first employ GCN as a primary classifier to handle textual data, obtaining the initial predicted results and the membership matrix. Depending on the idea of processing in threes, these results were divided into acceptance, rejection, and subdivision regions, respectively. For the subdivision region, we introduce SVM as a secondary classifier to process objects with poor conformability and distinguishability, which can reduce the uncertainty of prediction results and improve the overall performance of text classification. A series of experiments based on several benchmark datasets extensively evaluated the proposed method. The results demonstrate the validity of the approach and show a significant improvement over popular baseline text classification models.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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