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</title>
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<namePart>Flores, Anibal</namePart>
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<namePart>Tito Chura, Hugo</namePart>
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<namePart>Zea Rospigliosi, Lissethe</namePart>
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<abstract displayLabel="Summary">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</abstract>
<note type="statement of responsibility">Anibal Flores, Hugo Tito-Chura, Lissethe Zea-Rospigliosi</note>
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<topic>Inteligencia artificial</topic>
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<topic>Predicciones</topic>
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<topic>Análisis de datos</topic>
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<title>Revista Iberoamericana de Inteligencia Artificial</title>
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<publisher> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</publisher>
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<identifier type="issn">1988-3064</identifier>
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<text>13/03/2023 Volumen 26 Número 71 - marzo 2023 , pp. 46-58</text>
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