Publicaciones

@article{TRONCOSOGARCIA2023169,

title = {A new approach based on association rules to add explainability to time series forecasting models},

author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S1566253523000295},

doi = {https://doi.org/10.1016/j.inffus.2023.01.021},

issn = {1566-2535},

year  = {2023},

date = {2023-01-01},

journal = {Information Fusion},

volume = {94},

pages = {169-180},

abstract = {Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articleh,

title = {A Novel Distributed Forecasting Method Based on Information Fusion and Incremental Learning for Streaming Time Series},

author = {Laura Melgar-García and David Gutiérrez-Avilés and Cristina Rubio-Escudero and Alicia Troncoso},

doi = {10.2139/ssrn.4326600},

year  = {2023},

date = {2023-01-01},

journal = {SSRN Electronic Journal},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{isa22,

title = {Ten quick tips for biomarker discovery and validation analyses using machine learning},

author = {Ramon Diaz-Uriarte and Elisa Gómez de Lope and Rosalba Giugno and Holger Fröhlich and Petr V. Nazarov and Isabel A. Nepomuceno-Chamorro and Armin Rauschenberger and Enrico Glaab},

url = {https://doi.org/10.1371/journal.pcbi.1010357},

doi = {10.1371/journal.pcbi.1010357},

year  = {2022},

date = {2022-01-01},

journal = {PLOS Computational Biology},

volume = {18},

number = {8},

pages = {1-17},

publisher = {Public Library of Science},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{app12083987,

title = {MOMIC: A Multi-Omics Pipeline for Data Analysis, Integration and Interpretation},

author = {Laura Madrid-Márquez and Cristina Rubio-Escudero and Beatriz Pontes and Antonio González-Pérez and José C. Riquelme and Maria E. Sáez},

url = {https://www.mdpi.com/2076-3417/12/8/3987},

doi = {10.3390/app12083987},

issn = {2076-3417},

year  = {2022},

date = {2022-01-01},

journal = {Applied Sciences},

volume = {12},

number = {8},

abstract = {Background and Objectives: The burst of high-throughput omics technologies has given rise to a new era in systems biology, offering an unprecedented scenario for deriving meaningful biological knowledge through the integration of different layers of information. Methods: We have developed a new software tool, MOMIC, that guides the user through the application of different analysis on a wide range of omic data, from the independent single-omics analysis to the combination of heterogeneous data at different molecular levels. Results: The proposed pipeline is developed as a collection of Jupyter notebooks, easily editable, reproducible and well documented. It can be modified to accommodate new analysis workflows and data types. It is accessible via momic.us.es, and as a docker project available at github that can be locally installed. Conclusions: MOMIC offers a complete analysis environment for analysing and integrating multi-omics data in a single, easy-to-use platform.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/jigpal/jzac033,

title = {Data streams classification using deep learning under different speeds and drifts},

author = {Pedro Lara-Benítez and Manuel Carranza-García and David Gutiérrez-Avilés and José C Riquelme},

url = {https://doi.org/10.1093/jigpal/jzac033},

doi = {10.1093/jigpal/jzac033},

issn = {1367-0751},

year  = {2022},

date = {2022-01-01},

journal = {Logic Journal of the IGPL},

abstract = {Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The design of the asynchronous dual-pipeline DL framework allows making predictions of incoming instances and updating the model simultaneously, using two separate layers. The aim of this work is to assess the performance of different types of DL architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time series datasets that are simulated as streams at different speeds. In addition, we evaluate how the different architectures react to concept drifts typically found in evolving data streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency, but are also the most sensitive to concept drifts.},

note = {jzac033},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articleb,

title = {Ten quick tips for biomarker discovery and validation analyses using machine learning},

author = {Ramon Diaz-Uriarte and Elisa Gomez de Lope and Rosalba Giugno and Holger Fröhlich and Petr Nazarov and Isabel Nepomuceno-Chamorro and Armin Rauschenberger and Enrico Glaab},

doi = {10.1371/journal.pcbi.1010357},

year  = {2022},

date = {2022-01-01},

journal = {PLOS Computational Biology},

volume = {18},

pages = {e1010357},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articlei,

title = {Sa1267: INTRODUCING JOINT MODELING TECHNIQUES FOR PERSONALIZED PREDICTIONS IN CELIAC DISEASE},

author = {Lidia Serrano and Alfonso Rodríguez-Herrera and Carles Serrat and Cristina Rubio-Escudero},

doi = {10.1016/S0016-5085(22)60867-9},

year  = {2022},

date = {2022-01-01},

journal = {Gastroenterology},

volume = {162},

pages = {S-361},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articlej,

title = {A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture},

author = {Laura Melgar-García and David Gutiérrez-Avilés and Maria Teresa Godinho and Rita Espada and Isabel Brito and Francisco Martínez-Álvarez and Alicia Troncoso and Cristina Rubio-Escudero},

doi = {10.1016/j.neucom.2021.06.101},

year  = {2022},

date = {2022-01-01},

journal = {Neurocomputing},

volume = {500},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articlek,

title = {MOMIC: A Multi-Omics Pipeline for Data Analysis, Integration and Interpretation},

author = {Laura Madrid-Márquez and Cristina Rubio-Escudero and Beatriz Pontes and Antonio González-Pérez and José Riquelme and Maria Saez},

doi = {10.3390/app12083987},

year  = {2022},

date = {2022-01-01},

journal = {Applied Sciences},

volume = {12},

pages = {3987},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{article,

title = {Object detection using depth completion and camera-LiDAR fusion for autonomous driving},

author = {Manuel Carranza-García and F. Galán-Sales and José María Luna-Romera and José Riquelme},

doi = {10.3233/ICA-220681},

year  = {2022},

date = {2022-01-01},

journal = {Integrated Computer-Aided Engineering},

volume = {29},

pages = {1-18},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articlec,

title = {OCEAn: Ordinal Classification with an Ensemble Approach},

author = {Belén Vega and Isabel Nepomuceno-Chamorro and Cristina Rubio-Escudero and José Riquelme},

doi = {10.1016/j.ins.2021.08.081},

year  = {2021},

date = {2021-01-01},

journal = {Information Sciences},

volume = {580},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articled,

title = {Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market},

author = {Belén Vega and Cristina Rubio-Escudero and Isabel Nepomuceno-Chamorro and Angel Arcos-Vargas},

doi = {10.3390/app11136097},

year  = {2021},

date = {2021-01-01},

journal = {Applied Sciences},

volume = {11},

pages = {6097},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articlel,

title = {A data mining based clinical decision support system for survival in lung cancer},

author = {Beatriz Pontes and Francisco Núñez-Benjumea and Cristina Rubio-Escudero and Alberto Moreno Conde and Isabel Nepomuceno and Jesús Moreno and Jon Cacicedo and J. M. Praena-Fernández and German Rodriguez and Carlos Parra Calderón and Blas León and E. Campo and Felipe Couñago and José Riquelme and Jose Lopez Guerra},

doi = {10.5603/RPOR.a2021.0088},

year  = {2021},

date = {2021-01-01},

journal = {Reports of Practical Oncology and Radiotherapy},

volume = {26},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articlem,

title = {OCEAn: Ordinal Classification with an Ensemble Approach},

author = {Belén Vega and Isabel Nepomuceno-Chamorro and Cristina Rubio-Escudero and José Riquelme},

doi = {10.1016/j.ins.2021.08.081},

year  = {2021},

date = {2021-01-01},

journal = {Information Sciences},

volume = {580},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articlen,

title = {Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market},

author = {Belén Vega and Cristina Rubio-Escudero and Isabel Nepomuceno-Chamorro and Angel Arcos-Vargas},

doi = {10.3390/app11136097},

year  = {2021},

date = {2021-01-01},

journal = {Applied Sciences},

volume = {11},

pages = {6097},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articleo,

title = {Generating a seismogenic source zone model for the Pyrenees: A GIS-assisted triclustering approach},

author = {José-Lázaro Amaro-Mellado and Laura Melgar-García and Cristina Rubio-Escudero and David Gutiérrez-Avilés},

doi = {10.1016/j.cageo.2021.104736},

year  = {2021},

date = {2021-01-01},

journal = {Computers & Geosciences},

volume = {150},

pages = {104736},

keywords = {},

pubstate = {published},

tppubtype = {article}

}