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Prediction of research project execution using data augmentation and deep learning
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Title: Prediction of research project execution using data augmentation and deep learning / Anibal Flores, Hugo Tito-Chura, Lissethe Zea-RospigliosiAuthor: Flores, Anibal ‌
Notes: Sumario: Since most of the dataset prediction features are of the nominal type (true or false), this paper proposes a simple novel data augmentation technique for this type of features. Taking as inspiration the input data type of a neural network, the proposal data augmentation technique considers nominal features as numeric, and obtain random values close to them to generate synthetic records. The results show that most of deep learning models with data augmentation significantly outperform models with just class balancing in terms of accuracy, precision, f1-score and specificity, being the main improvements of 17.39%, 80.00%, 25.00% and 20.00% respectivelyRelated records: En: Revista Iberoamericana de Inteligencia Artificial. - : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018- = ISSN 1988-3064. - 13/03/2023 Volumen 26 Número 71 - marzo 2023 , pp. 46-58Materia / lugar / evento: Inteligencia artificial ‌Predicciones ‌Gestión de datos ‌Análisis de datos ‌Otros autores: Tito Chura, Hugo ‌
Zea Rospigliosi, Lissethe ‌
Other categories: 922.134
Rights: In Copyright (InC)
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