Adaptive Learning from Evolving Data Streams

By Albert Bifet, Ricard Gavaldà. 2009

489 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Memory Efficient Experience Replay for Streaming Learning (2018, background): “Many are based on Hoeffding Decision Trees [6], [18], [25], [36],”
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (2019, background): “Hoeffding Decision Trees [4, 16, 21, 27, 28] incrementally grow decision trees over time under the Hoeffding bound theoretical guarantees.”
Estimation of energy consumption in machine learning (2019, background): “On the other hand, for the random tree dataset, both algorithms obtain similar levels of accuracy (less than 0.5% difference) but the VFDT consumes significantly less energy compared to HAT.”
Extremely Fast Decision Tree (2018, methodology): “The de facto standard for learning decision trees from streaming data is Hoeffding Tree (HT) [11], which is used as a base for many state-of-the-art drift learners [3, 6, 8, 10, 16, 18, 24].”
Learning under Concept Drift: A Review (2019, background): “It is worth noting that many concept drift adaptation methods/algorithms in the literature are derived from or combined with ADWIN, such as [32]–[35].”
Characterizing concept drift (2015, background): “Of the tree based classiﬁers, only HoeffdingAdaptiveTree behaves as expected, as shown in Fig.”
Stream-51: Streaming Classification and Novelty Detection from Videos (2020, background): “Online learning in a single pass with severe memory and compute constraints, also called streaming learning [2, 5, 10, 13, 14, 20, 35], more closely matches how embedded agents must learn and make inferences in real-time.”
Online Machine Learning in Big Data Streams (2018, methodology): “rvals. Another difﬁculty is caused by nonstationary data, since each new child node is processed based on a new portion of data from the stream. Decision trees over evolving streams are considered in [26, 24, 31]. We give an overview of general methods for nonstationary data in Section 6.2. 3.4 Ense”
From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare (2015, methodology): “learners [105], which has been used for sleep apnoea monitoring [106].”
Scalable and efficient multi-label classification for evolving data streams (2012, background): “The most significant are the following, as outlined in Bifet and Gavaldà (2009): – process an example at a time, and inspect it only once (at most); – be ready to predict at any point; – data may be evolving over time; and – expect an infinite stream, but process it under finite resources (time and…”