Examinando por Autor "Moles, Luis"

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  • Miniatura
    Ítem
    Exploring data augmentation and active learning benefits in imbalanced datasets
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-06) Moles, Luis; Andres, Alain; Echegaray, Goretti; Boto Sánchez, Fernando
    Despite the increasing availability of vast amounts of data, the challenge of acquiring labeled data persists. This issue is particularly serious in supervised learning scenarios, where labeled data are essential for model training. In addition, the rapid growth in data required by cutting-edge technologies such as deep learning makes the task of labeling large datasets impractical. Active learning methods offer a powerful solution by iteratively selecting the most informative unlabeled instances, thereby reducing the amount of labeled data required. However, active learning faces some limitations with imbalanced datasets, where majority class over-representation can bias sample selection. To address this, combining active learning with data augmentation techniques emerges as a promising strategy. Nonetheless, the best way to combine these techniques is not yet clear. Our research addresses this question by analyzing the effectiveness of combining both active learning and data augmentation techniques under different scenarios. Moreover, we focus on improving the generalization capabilities for minority classes, which tend to be overshadowed by the improvement seen in majority classes. For this purpose, we generate synthetic data using multiple data augmentation methods and evaluate the results considering two active learning strategies across three imbalanced datasets. Our study shows that data augmentation enhances prediction accuracy for minority classes, with approaches based on CTGANs obtaining improvements of nearly 50% in some cases. Moreover, we show that combining data augmentation techniques with active learning can reduce the amount of real data required.
  • Miniatura
    Ítem
    On the use of machine learning for predicting femtosecond laser grooves in tribological applications
    (Elsevier Ltd, 2024-12) Moles, Luis; Llavori, Iñigo; Aginagalde, Andrea; Echegaray, Goretti; Bruneel, David; Boto Sánchez, Fernando; Zabala, Alaitz
    Femtosecond laser surface texturing is gaining increased interest for optimizing tribological behaviour. However, the laser surface texturing parameter selection is often conducted through time-consuming and inefficient trial-and-error processes. Although machine learning emerges as an interesting option, multitude of models exists, and determining the most suitable one for predicting femtosecond laser textures remains uncertain. Furthermore, the absence of open-source implementations and the expertise required for their utilization hinders their adoption within the tribology community. In this study, two novel inverse modelling approaches for the optimal prediction of femtosecond laser parameters are proposed, based on the results of a comparison between six different machine learning models conducted within this research. The entire development relies on open-source tools, and the models employed are shared, with the aim of democratizing these techniques and facilitating their adoption by non-expert users within the tribology community.