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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2021
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| 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 |