Summary of Resilience to the Flowing Unknown: An Open Set Recognition Framework For Data Streams, by Marcos Barcina-blanco et al.
Resilience to the Flowing Unknown: an Open Set Recognition Framework for Data Streams
by Marcos Barcina-Blanco, Jesus L. Lobo, Pablo Garcia-Bringas, Javier Del Ser
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a novel approach to designing resilient AI systems capable of handling complex and dynamic scenarios with continuously generated data streams. The paper explores the concept of Open Set Recognition, which is essential for addressing the “over-occupied space” problem in streaming scenarios where models must operate without seeing patterns during training. A hybrid framework combining classification and clustering is proposed to tackle this challenge. The framework is evaluated on a benchmark comprising different classification datasets with varying ratios of known to unknown classes, demonstrating improved performance over individual incremental classifiers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI systems are becoming increasingly important in modern digital applications. However, they face challenges when handling continuously generated data streams. This paper solves the “over-occupied space” problem by proposing an Open Set Recognition framework that combines classification and clustering. The framework is tested on a benchmark with different datasets and shows better performance than individual classifiers. |
Keywords
» Artificial intelligence » Classification » Clustering
