Ensemble learning for data stream analysis: A survey

By Bartosz Krawczyk, Leandro L. Minku, João Gama, Jerzy Stefanowski, Michal Wozniak. 2017

990 citations recorded by the source corpus.

A comprehensive survey of ensemble approaches for data stream analysis.Taxonomy of ensemble algorithms for various data stream mining tasks.Discussion of open research problems and lines of future research. In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of several new proposed stream algorithms, ensembles play an important role, in particular for non-stationary environments. This paper surveys research on ensembles for data stream classification as well as regression tasks. Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semi-supervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research.

Subfields: Artificial Intelligence, Signal Processing

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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].”
A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, background): “In the literature, there are many reviews about ensemble learning methods, and techniques (Krawczyk et al., 2017; Sagi and Rokach, 2018; Dong et al., 2020).”
Learning under Concept Drift: A Review (2019, background): “Besides these two publications, four related survey papers [6], [7], [10], [11] have also provided valuable insights into how to address concept drift, but their specific research focus is only on data stream learning, rather than analyzing concept drift adaptation algorithms and understanding conce”
FAHT: An Adaptive Fairness-aware Decision Tree Classifier (2019, background): “The main challenge for learning in a stream environment is the so called, concept-drifts, i.e., changes in the joint data distribution over time [Krawczyk et al. , 2017; Aggarwal, 2007; Gama et al. , 2014].”
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects (2022, background): “Other ensemble learning reviews in the literature include those that focus on intrusion detection sys-77 tems [37], sentiment analysis [38], and big data [39].”
From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors (2022, background): “This assumption is based on the fact that each learner has capabilities in solving specific problems [44].”
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary (2018, methodology): “For example, we discuss the extensions of SMOTE to other learning paradigms, such as streaming data (Krawczyk et al., 2017; Brzezinski & Stefanowski, 2017; Hoens et al., 2011), incremental learning (Ditzler, Polikar, & Chawla, 2010), concept drift (Hoens & Chawla, 2012; Hoens, Polikar, & Chawla, 201”