New Options for Hoeffding Trees

By Bernhard Pfahringer, G. Holmes, Richard Kirkby. 2007

159 citations recorded by the source corpus.

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In Artificial Intelligence

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].”
Ensemble learning for data stream analysis: A survey (2017, background): “Hoeffding Option Trees (HOT) can be seen as an extension of Kirkby’s Option Tree [144].”
Life-long learning based on dynamic combination model (2017, background): “Hoeffding tree is studied in [4] and [22] for processing high-speed data streams.”
Speeding-Up Hoeffding-Based Regression Trees With Options (2011, methodology): “Following Pfahringer et al. (2007), another variant of option trees on streams (Liu et al., 2009) is based on the CVFDT algorithm (Hulten et al., 2001) in which alternate trees are initiated every time a new splitting test is found to be better than the existing one.”
The CART decision tree for mining data streams (2014, methodology): “More specifically, our result should replace the Hoeffding's bound used incorrectly in algorithms like the Concept-adapting Very Fast Decision Trees (CVFDT) [17] or Hoeffding Option Trees (HOT) [24,5].”
A Survey on Ensemble Learning for Data Stream Classification (2017, methodology): “An example is the Option Hoeffding Trees [Pfahringer et al. 2007], which is basically an algorithm that could be interpreted as either a decision tree or an ensemble.”
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering (2010, unknown): “Hoeffding Option Trees [34] are regular Hoeffding trees containing additional option nodes that allow several tests to be applied, leading to multiple Hoeffding trees as separate paths.”