2024-03-082024-03-082022-09-12PIRES, 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.http://repositorio.bc.ufg.br/tede/handle/tede/13312The 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.Attribution-NonCommercial-NoDerivatives 4.0 InternationalGPUFlorestas de decisãoTextoDecision forestsTextCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOTese