Dynamic weighted majority: a new ensemble method for tracking concept drift

By J. Kolter, M. Maloof. Third IEEE International Conference on Data Mining, 2003

440 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c1a880d5f744d2d26b2ab404e7b60791632edd3c

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Citations in the corpus

A survey of multiple classifier systems as hybrid systems (2014, methodology): “Both static [111–113] and dynamic [114–116] classifier special-”
Online Machine Learning in Big Data Streams (2018, background): “olving learners, by contrast, do not aim to detect changes but rather maintain the most accurate models at each time step. Evolving learners are method-speciﬁc, most of them based on ensemble methods [225, 139]. A few papers [168, 171] give overviews of different types of environmental changes and c”
Combination of information entropy and ensemble classification for detecting concept drift in data stream (2018, methodology): “Dynamic Weighted Majority (DWM) [12] is a method that is based on ensemble classifier.”
Tracking recurring contexts using ensemble classifiers: an application to email filtering (2009, background): “This is an assumption often made in such problems (see STAGGER Concepts [9] or Moving Hyperplanes [27]) mainly because the only way to detect drift is by observing mistakes of the model in a small time window, and then adapt in order to achieve better performance in the next batch of data.”
The problem of concept drift: definitions and related work (2004, methodology): “These include rule-based learning (Schlimmer and Granger, 1986; Widmer and Kubat, 1993, 1996; Wang et al., 2003), decision trees, including their incremental versions (Harries and Sammut, 1998; Hulten et al., 2001; Street and Kim, 2001; Kolter and Maloof, 2003; Stanley, 2003; Wang et al., 2003), Naï”
A framework for generating data to simulate changing environments (2007, methodology): “Bespoke online classification models [5, 6, 32], as well as classifier ensembles [21,29] have been proposed for changing environments [22].”
Ensembles in adversarial classification for spam (2009, background): “[3] extended this work by dynamically creating and removing weighted experts in response to changes in performance.”
Adaptive random forests for evolving data stream classification (2017, methodology): “Another approach to deal with concept drifts while using an ensemble of classifiers is to constantly reset low performance classifiers [32,20,27].”
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts (2007, methodology): “In previous work, we evaluated DWM using two synthetic data sets, the STAGGER concepts (Schlimmer and Granger, 1986) and the SEA concepts (Street and Kim, 2001), achieving the best published results (Kolter and Maloof, 2003).”
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift (2010, methodology): “Kolter and Maloof (2007) mention that changes in the target class represent changes in the posterior probabilities P (wi|x) and in the class-conditional pdfs p(x|wi).”
Collaborative filtering with temporal dynamics (2009, background): “: “customer preferences change as new products and services become available” [7].”