GCUL: Ambiguity Identification in Text Emotion Classification via Cluster-Guided Learning
Selective classification enables a model to abstain from predictions on uncertain instances, but existing approaches typically reject them through confidence scores, predefined coverage constraints or instance-level distance measures. These approaches may overlook the collective geometric structure of difficult samples in learned representation spaces. We propose Guided Clustering-based Uncertain Learning (GCUL), a geometric-guided selective classification framework that identifies misclassified and ambiguous instances as a potential confusion attractor in the representation space. GCUL uses a three-phase procedure to initialize, cluster, and explicitly relabel this uncertain region, allo...
| Authors | Zhongqi Fan, Tianyou Zhang, Fei Chen |
|---|---|
| Primary category | stat.ML |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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