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Fecha
2025-09-29
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Citas
Resumen
Deep learning models have achieved remarkable success in vision and language, yet their application to tabular data remains challenging. This work introduces PermGrad, a Hybrid Neural Network (HyNN) framework that integrates tabular and image-based representations to enhance both predictive performance and interpretability. Tabular data are transformed into synthetic images using the TINTO methodology, enabling convolutional neural networks (CNNs) to capture local feature interactions, while a multilayer perceptron (MLP) branch models global relationships. Interpretability is achieved through three complementary components: permutation-based feature importance for the MLP branch, Grad-CAM saliency mapping for the CNN branch, and a novel PermGrad score, which integrates branch-level contributions via pruning-based analyses. Extensive experiments on five heterogeneous datasets, covering classification and regression tasks, demonstrate that the proposed hybrid approach provides competitive performance against classical machine learning baselines, while offering transparent, featurelevel, and branch-level contributions. The study highlights design trade-offs between interpretability and computational cost, and outlines directions for future extensions including transformerbased backbones and advanced fusion strategies.
Descripción
Categorías UNESCO
Palabras clave
Deep Learning, Hybrid Neural Network, Synthetic images, TINTOlib, Tabular-to-images, Interpretability
Citación
Escalera González, Felipe. Trabajo Fin de Máster: PermGrad: Interpretable Hybrid Neural Networks with Synthetic Images for Tabular Data. Universidad Nacional de Educación a Distancia (UNED) 2025
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial