Online bagging and boosting

By Nikunj C. Oza, Stuart J. Russell. 2005

1,024 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Counterfactual Estimation and Optimization of Click Metrics for Search Engines (2014, background): “As suggested by the same authors [5], in reality it may be better to use normal approximation theory to obtain confidence intervals.”
Fraud detection system: A survey (2016, background): “Adaptive learning algorithms can be seen as advanced incremental learning algorithms that are able to update detection model for streaming evolving data over time (Bolton and Hand, 2002; Phua et al., 2005; Oza, 2005; Gama et al., 2013).”
Personalized Recommendation via Parameter-Free Contextual Bandits (2015, methodology): “Therefore, in our problem setting, we apply the online bootstrap method to solve the contextual bandit problem [23].”
InterruptMe: designing intelligent prompting mechanisms for pervasive applications (2014, methodology): “We investigate the performance of three online classifiers, naive Bayesian, Hoeffding tree, an online tree-based algorithm, and OzaBoost, an online version of a boosting approach [29].”
Bootstrapping data arrays of arbitrary order (2011, methodology): “Oza (2001) takes Wij to be independent Poisson random variables with mean 1.”
Weak Convergence of the Wild Bootstrap for the Aalen–Johansen Estimator of the Cumulative Incidence Function of a Competing Risk (2013, methodology): “Moreover, the wild bootstrap has also been used in several research papers on machine learning, see, e.g. Oza & Russell (2001) and Lee & Clyde (2004).”
A Contextual Bandit Bake-off (2018, neutral): “…the posterior distribution over policies via the online Bootstrap (Agarwal et al., 2014; Eckles and Kaptein, 2014; Osband and Van Roy, 2015; Oza and Russell, 2001; Qin et al., 2013), and explores in a Thompson sampling fashion, by computing the probability over policies of each action in a…”
A framework for generating data to simulate changing environments (2007, background): “KEY WORDS Machine Learning, Changing environments, Concept drift, Population drift, Artificial data, Simulated data.”
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift (2010, methodology): “In this work, we will adopt the definition of incremental learning given by Polikar et al. (2001), as it is more clear than the general notions given by authors such as Littlestone (1988) and is very similar to the definitions given by Schaal and Atkeson (1998) and Kasabov (2003).”
Learn$^{++}$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes (2009, methodology): “online boosting [57], [58], combining single-class subnets [59], streaming ensemble algorithms (SEAs) [60], and dynamic weighted majority (DWM) [61].”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, methodology): “Their algorithm, is essentially a modification of Online Bagging [179], in which the sampling rate of the instances belonging to one class is determined adaptively based on the current imbalance status and classification performance.”
A comprehensive survey of anomaly detection techniques for high dimensional big data (2020, background): “Oza and Russell [126] proposed two online variations of bag-ging and boosting for data streams.”
Online sketching hashing (2015, methodology): “For example, an online version of Bagging and Boosting was proposed in [29] to adapt the original AdaBoost and Bagging algorithms for learning from the stream data.”
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, methodology): “For classification, there are improved versions of bagging such as online bagging (and boosting) [44], double-bagging [45], wagging [46].”