RAZONAMIENTO APROXIMADO E INTELIGENCIA ARTIFICIAL
RAZONAMIENTO APROXIMADO E INTE
María José del
Jesús Díaz
Publications by the researcher in collaboration with María José del Jesús Díaz (87)
2022
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Una visión actual de la inteligencia artificial: Recorrido histórico, datos y aprendizaje, confiabilidad y datos
El derecho y la inteligencia artificial (Editorial Universidad de Granada), pp. 51-80
2020
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An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges
Neurocomputing, Vol. 404, pp. 93-107
2019
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A Showcase of the Use of Autoencoders in Feature Learning Applications
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Dealing with difficult minority labels in imbalanced mutilabel data sets
Neurocomputing, Vol. 326-327, pp. 39-53
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Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to?
IEEE Computational Intelligence Magazine, Vol. 14, Núm. 1, pp. 69-81
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REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization
Neurocomputing, Vol. 326-327, pp. 110-122
2018
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A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
Information Fusion, Vol. 44, pp. 78-96
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A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines
XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018): avances en Inteligencia Artificial. 23-26 de octubre de 2018 Granada, España
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A unifying analysis for the supervised descriptive rule discovery via the weighted relative accuracy
Knowledge-Based Systems, Vol. 139, pp. 89-100
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Atipicidad: medida de calidad clave dentro del descubrimiento de reglas descriptivas supervisadas
XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018): avances en Inteligencia Artificial. 23-26 de octubre de 2018 Granada, España
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Pareto based ensemble with feature and instance selection for learning from multi-class imbalanced datasets
XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018): avances en Inteligencia Artificial. 23-26 de octubre de 2018 Granada, España
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Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository
Neurocomputing, Vol. 289, pp. 68-85
2017
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KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining
International Journal of Computational Intelligence Systems, Vol. 10, Núm. 1, pp. 1238-1249
2016
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A View on Fuzzy Systems for Big Data: Progress and Opportunities
International Journal of Computational Intelligence Systems, Vol. 9, pp. 69-80
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Multilabel classification: Problem analysis, metrics and techniques
Springer International Publishing, pp. 1-194
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On the impact of dataset complexity and sampling strategy in multilabel classifiers performance
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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R ultimate multilabel dataset repository
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2015
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Addressing imbalance in multilabel classification: Measures and random resampling algorithms
Neurocomputing, Vol. 163, pp. 3-16
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Addressing overlapping in classification with imbalanced datasets: A first multi-objective approach for feature and instance selection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation
Knowledge-Based Systems, Vol. 89, pp. 385-397