UM Research Repository
metadata only
Performance evaluation of multilabel emotion classification using data augmentation techniques
One of the challenges of emotion classification is the existence of low annotated datasets, that makes the task more complex. Certain existing datasets often suffer from imbalanced data for the emotion classes. Several data augmentation approaches can help to overcome the challenges regarding imbala
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Peer review UpCwGBfq#787070
- Prototypical graph based deep label propagation with semantic augmentation for cross-subject and cross-session EEG emotion recognition.#392771
- VA-DPO: Valence-Arousal Direct Preference Optimization for Controllable Emotion Generation in Language Models#658952
- VA-DPO: Valence-Arousal Direct Preference Optimization for Controllable Emotion Generation in Language Models#656816
- SMOTE: Synthetic Minority Over-sampling Technique#6310
Record · ID 1226524
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive