Scalable and efficient multi-label classification for evolving data streams
By Jesse Read, Albert Bifet, G. Holmes, Bernhard Pfahringer. 2012
147 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tyler L. Hayes
- Christopher Kanan
- Min-Ling Zhang
- Bartosz Krawczyk
- Zhi-Hua Zhou
- Sebastián Ventura
- Jerzy Stefanowski
- Xin Geng
In Information Systems
- Min-Ling Zhang
- Zhi-Hua Zhou
- Xin Geng
- Bartosz Krawczyk
- João Gama
- Michal Wozniak
- Sebastián Ventura
- Jerzy Stefanowski
Citations in the corpus
- Memory Efficient Experience Replay for Streaming Learning (2018, neutral): “[37], [38], [39], [56], [63] or ensemble methods [7], [9], [27], [44], [46], [51], [54], [61], [70], [72].”
- Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (2019, background): “The ability to do online learning under these constraints from non-stationary data streams in a single pass is known as streaming learning [2, 4, 16, 19, 22, 23, 27, 43].”
- Ensemble learning for data stream analysis: A survey (2017, background): “[151], but there is not an abundance of work that follow it, especially from the ensemble point of view.”
- A Tutorial on Multilabel Learning (2015, methodology): “The work of Sechidis et al. [2011] can be referenced in which two stratification methods are proposed.”
- Label Distribution Learning (2013, background): “3 gives three cases of the decision regions for a typical two-label problem, where red represents the decision region of the label y 1 , and yellow represents that of the label y 2 .”
- A Review on Multi-Label Learning Algorithms (2014, background): “Existing works model concept drift by updating the classifiers significantly whenever a new batch of examples arrive [68], taking the fading assumption that the influence of past data gradually declines as time evolves [53], [78], or maintaining a change detector alerting whenever a concept drift is”
- Stream-51: Streaming Classification and Novelty Detection from Videos (2020, background): “Online learning in a single pass with severe memory and compute constraints, also called streaming learning [2, 5, 10, 13, 14, 20, 35], more closely matches how embedded agents must learn and make inferences in real-time.”