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SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning
arXiv – CS AI|Umid Suleymanov, Murat Kantarcioglu, Kevin S Chan, Michael De Lucia, Kevin Hamlen, Latifur Khan, Sharad Mehrotra, Ananthram Swami, Bhavani Thuraisingham|
🤖AI Summary
Researchers introduce SPRINT, the first Few-Shot Class-Incremental Learning (FSCIL) framework designed specifically for tabular data domains like cybersecurity and healthcare. The system achieves 77.37% accuracy in 5-shot learning scenarios, outperforming existing methods by 4.45% through novel semi-supervised techniques that leverage unlabeled data and confidence-based pseudo-labeling.
Key Takeaways
- →SPRINT is the first FSCIL framework specifically designed for tabular data streams rather than computer vision applications.
- →The system uses confidence-based pseudo-labeling to improve learning from limited labeled data in domains like cybersecurity and healthcare.
- →SPRINT achieves state-of-the-art 77.37% average accuracy in 5-shot learning, outperforming baselines by 4.45%.
- →The framework leverages abundant unlabeled data and low storage costs typical of tabular domains to retain knowledge without forgetting.
- →Extensive testing across six benchmarks demonstrates cross-domain robustness in cybersecurity, healthcare, and ecological applications.
#machine-learning#few-shot-learning#tabular-data#cybersecurity#healthcare#incremental-learning#semi-supervised#sprint#research#artificial-intelligence
Read Original →via arXiv – CS AI
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