Leveraging Bagging for Evolving Data Streams
By Albert Bifet, G. Holmes, Bernhard Pfahringer. ECML/PKDD, 2010
386 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/ba53cac9ec1762768b3d6f372065afeca2b16904
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
- Albert Bifet
- Indrė Žliobaitė
- Francisco Herrera
- Bartosz Krawczyk
- Jerzy Stefanowski
- João Gama
- Bernhard Pfahringer
- G. Holmes
In Signal Processing
- João Gama
- Jerzy Stefanowski
- Albert Bifet
- Indrė Žliobaitė
- Dariusz W. Brzezinski
- Heitor Murilo Gomes
- Fabrício Enembreck
- Jean Paul Barddal
Citations in the corpus
- Online Machine Learning in Big Data Streams (2018, methodology): “t subsets of instances and attributes [187]. The simplest example is bagging where several base models are trained based on samples with replacement. Sampling with replacement can be simulated online [181, 28]. A special ensemble technique, the online random forest algorithm is described in [70]. A ”
- Ensemble learning for data stream analysis: A survey (2017, methodology): “On the other hand, our literature survey shows that only few authors directly consider promoting diversity while constructing an ensemble or rebuilding them in the moment of detecting drifts, see e.g. DDD ensemble [126] or other generalizations of online bagging such as [16] .”
- Adaptive random forests for evolving data stream classification (2017, methodology): “Bagging variants includes Online Bagging (OzaBag) [35] and Leveraging Bagging (LevBag) [13].”
- A Survey on Ensemble Learning for Data Stream Classification (2017, methodology): “Besides using a clustering method (AnyMeans) capable of discovering non-spherical clusters SluiceBoxAM can be used with other ensemble classifiers, for example, in [Parker and Khan 2015] authors report the performance of SluiceBoxAM combined with the leveraging bagging algorithm [Bifet et al. 2010a].”
- Learning under Concept Drift: A Review (2019, methodology): “4 Poker-Hand [108] 1025010 10 10 [13], [32], [64], [74], [104]”
- A survey on data preprocessing for data stream mining: Current status and future directions (2017, background): “There also exist hybrid approaches that combine both of these solutions within the ensemble structure [43, 44].”
- Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm (2014, methodology): “Recently, Bifet et al. [26] introduced a modification of Oza's algorithm called Leveraging Bagging, which aims to add more randomization to the input and output of the base classifiers.”
- A survey on concept drift adaptation (2014, methodology): “RAM-hours [Bifet et al. 2010] is a one-dimensional measure of the computational resources used by streaming algorithms, based on rental cost options of cloud computing services.”