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. |
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| 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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| ISSN: | 1099-4300 |
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| DOI: | 10.3390/e23070859 |
