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Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams.

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Title: Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams.
Authors: AlQabbany AO; Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.; King Abdulaziz City for Science and Technology, Riyadh 12371, Saudi Arabia., Azmi AM; Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Source: Entropy (Basel, Switzerland) [Entropy (Basel)] 2021 Jul 04; Vol. 23 (7). Date of Electronic Publication: 2021 Jul 04.
Publication Type: Journal Article
Journal Info: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101243874 Publication Model: Electronic Cited Medium: Internet ISSN: 1099-4300 (Electronic) Linking ISSN: 10994300 NLM ISO Abbreviation: Entropy (Basel) Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22AlQabbany+AO%22">AlQabbany AO</searchLink>; Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.; King Abdulaziz City for Science and Technology, Riyadh 12371, Saudi Arabia.<br /><searchLink fieldCode="AU" term="%22Azmi+AM%22">Azmi AM</searchLink>; Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
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  Data: <searchLink fieldCode="JN" term="%22101243874%22">Entropy (Basel, Switzerland)</searchLink> [Entropy (Basel)] 2021 Jul 04; Vol. 23 (7). <i>Date of Electronic Publication: </i>2021 Jul 04.
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        Value: 10.3390/e23070859
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              Text: 2021 Jul 04
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