Hierarchical Semantic Risk Minimization for Large-Scale Classification
Bibliographic record
Abstract
Hierarchical structures of labels usually exist in large-scale classification tasks, where labels can be organized into a tree-shaped structure. The nodes near the root stand for coarser labels, while the nodes close to leaves mean the finer labels. We label unseen samples from the root node to a leaf node, and obtain multigranularity predictions in the hierarchical classification. Sometimes, we cannot obtain a leaf decision due to uncertainty or incomplete information. In this case, we should stop at an internal node, rather than going ahead rashly. However, most existing hierarchical classification models aim at maximizing the percentage of correct predictions, and do not take the risk of misclassifications into account. Such risk is critically important in some real-world applications, and can be measured by the distance between the ground truth and the predicted classes in the class hierarchy. In this work, we utilize the semantic hierarchy to define the classification risk and design an optimization technique to reduce such risk. By defining the conservative risk and the precipitant risk as two competing risk factors, we construct the balanced conservative/precipitant semantic (BCPS) risk matrix across all nodes in the semantic hierarchy with user-defined weights to adjust the tradeoff between two kinds of risks. We then model the classification process on the semantic hierarchy as a sequential decision-making task. We design an algorithm to derive the risk-minimized predictions. There are two modules in this model: 1) multitask hierarchical learning and 2) deep reinforce multigranularity learning. The first one learns classification confidence scores of multiple levels. These scores are then fed into deep reinforced multigranularity learning for obtaining a global risk-minimized prediction with flexible granularity. Experimental results show that the proposed model outperforms state-of-the-art methods on seven large-scale classification datasets with the semantic tree.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".