Acelerando florestas de decisão paralelas em processadores gráficos para a classificação de texto
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2022-09-12
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Universidade Federal de Goiás
Resumo
The amount of readily available on-line text has grown exponentially, requiring efficient
methods to automatically manage and sort data. Automatic text classification provides means
to organize this data by associating documents with classes. However, the use of more data
and sophisticated machine learning algorithms has demanded an increasingly computing power.
In this work we accelerate a novel Random Forest-based classifier that has been shown to
outperform state-of-art classifiers for textual data. The classifier is obtained by applying the
boosting technique in bags of extremely randomized trees (forests) that are built in parallel to
improve performance. Experimental results using standard textual datasets show that the GPUbased
implementation is able to reduce the execution time by up to 20 times compared to an
equivalent sequential implementation.
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PIRES, J. C. B. Acelerando florestas de decisão paralelas em processadores gráficos para a classificação de texto. 2022. 91 f. Tese (Doutorado em Ciência Computação) - Instituto de Informática, Universidade Federal de Goiás, Goiânia, 2022.