Pesquisa de referências
Centro de Documentação

Prediction of research project execution using data augmentation and deep learning
Portada
MAP20230010006
Flores, Anibal ‌
Prediction of research project execution using data augmentation and deep learning / Anibal Flores, Hugo Tito-Chura, Lissethe Zea-Rospigliosi
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% respectively
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. 46-58
1. Inteligencia artificial ‌. 2. Predicciones ‌. 3. Gestión de datos ‌. 4. Análisis de datos ‌. I. Tito Chura, Hugo ‌. II. Zea Rospigliosi, Lissethe ‌. III. Título.