Beyond Incremental Processing: Tracking Concept Drift

By Jeffrey C. Schlimmer, Richard Granger. 1986

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Citations in the corpus

Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams (2020, background): “Besides the focus on continual learning in this work, our learner-evaluator framework generalizes to concept drift as well [38], for which we refer to an overview in [45, 12].”
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).”
Combination of information entropy and ensemble classification for detecting concept drift in data stream (2018, background): “The phenomenon of evolving data stream over time is known as a Concept Drift [6], where the term of concept drift was introduced by [7].”
Temporal recommendation on graphs via long- and short-term preference fusion (2010, background): “Unlike traditional concept drifting mining [20, 24] that focuses primarily on global pattern shifting over time, temporal recommendation focuses on local recommendation models for each user.”
DDD: A New Ensemble Approach for Dealing with Concept Drift (2012, background): “problem [32], but it allows the generation of different drifts,”
Collaborative filtering with temporal dynamics (2010, background): “15, 20 Temporal changes in customer preferences bring unique modeling challenges.”
Collaborative filtering with temporal dynamics (2009, background): “For example, a change in the family structure can drastically change shopping patterns.”
Decision trees for mining data streams (2006, unknown): “In the literature of machine learning, several methods have been presented to deal with time changing concepts [16,18,19,23,26,34].”
Mining time-changing data streams (2001, background): “Schlimmer and Granger's [ 23 ] STAGGER system was one of the first to explicitly address the problem of concept drift.”
Dynamic weighted majority: a new ensemble method for tracking concept drift (2003, methodology): “Predictive accuracy for DWM-NB on the STAGGER Concepts.”
BREEDS: Benchmarks for Subpopulation Shift (2020, background): “Robustness to distribution shift has been the focus of a long line of work in machine learning [SG86; WK93; KHA99; Shi00; SKM07; Qui+09; Mor+12; SK12].”