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

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data’s underlying distribution, a significant issue, especially when learning from data streams...

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Detalles Bibliográficos
Autores principales: AlQabbany, Abdulaziz O., Azmi, Aqil M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305386/
https://www.ncbi.nlm.nih.gov/pubmed/34356400
http://dx.doi.org/10.3390/e23070859

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