@article{10.1093/jigpal/jzae062,

title = {Embedded feature selection for neural networks via learnable drop layer},

author = {M J JimÉnez-Navarro and M MartÍnez-Ballesteros and I S Brito and F MartÍnez-Álvarez and G Asencio-CortÉs},

url = {https://doi.org/10.1093/jigpal/jzae062},

doi = {10.1093/jigpal/jzae062},

year  = {2024},

date = {2024-01-01},

journal = {Logic Journal of the IGPL},

volume = {N/A},

pages = {N/A},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/jigpal/jzae030,

title = {From simple to complex: a sequential method for enhancing time series forecasting with deep learning},

author = {M J Jiménez-Navarro and M Martínez-Ballesteros and F Martínez-Álvarez and A Troncoso and G Asencio-Cortés},

url = {https://doi.org/10.1093/jigpal/jzae030},

doi = {10.1093/jigpal/jzae030},

year  = {2024},

date = {2024-01-01},

journal = {Logic Journal of the IGPL},

volume = {N/A},

pages = {N/A},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.rineng.2024.103290,

title = {Explainable deep learning on multi-target time series forecasting: An air pollution use case},

author = {Manuel J. Jiménez-Navarro and Mario Lovrić and Simonas Kecorius and Emmanuel Karlo Nyarko and María Martínez-Ballesteros},

url = {https://doi.org/10.1016/j.rineng.2024.103290},

doi = {10.1016/j.rineng.2024.103290},

year  = {2024},

date = {2024-01-01},

journal = {Results in Engineering},

volume = {24},

pages = {103290},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3605098.3636191,

title = {Evolutionary Feature Selection for Time-Series Forecasting},

author = {Maria Lourdes Linares-Barrera and Manuel J. Jimenez-Navarro and Isabel Sofia Brito and José C. Riquelme and María Martínez-Ballesteros},

url = {https://doi.org/10.1145/3605098.3636191},

doi = {10.1145/3605098.3636191},

year  = {2024},

date = {2024-01-01},

journal = {Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing},

volume = {N/A},

pages = {395-399},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-62799-6_9,

title = {Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting},

author = {María Lourdes Linares-Barrera and Manuel J. Jiménez Navarro and José C. Riquelme and María Martínez-Ballesteros},

url = {https://doi.org/10.1007/978-3-031-62799-6_9},

doi = {10.1007/978-3-031-62799-6_9},

year  = {2024},

date = {2024-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {81-90},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.asoc.2024.111504,

title = {Explaining deep learning models for ozone pollution prediction via embedded feature selection},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://doi.org/10.1016/j.asoc.2024.111504},

doi = {10.1016/j.asoc.2024.111504},

year  = {2024},

date = {2024-01-01},

journal = {Applied Soft Computing},

volume = {157},

pages = {111504},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1109/sst61991.2024.10755283,

title = {Explainable Deep Learning with Embedded Feature Selection for Electricity Demand Forecasting},

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

url = {https://doi.org/10.1109/sst61991.2024.10755283},

doi = {10.1109/sst61991.2024.10755283},

year  = {2024},

date = {2024-01-01},

journal = {2024 International Conference on Smart Systems and Technologies (SST)},

volume = {N/A},

pages = {153-158},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-42519-6_30,

title = {Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation},

author = {Manuel J. Jiménez-Navarro and Belén Vega-Márquez and José María Luna-Romera and Manuel Carranza-García and María Martínez-Ballesteros},

url = {https://doi.org/10.1007/978-3-031-42519-6_30},

doi = {10.1007/978-3-031-42519-6_30},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {N/A},

pages = {319-328},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-18050-7_54,

title = {Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection},

author = {Manuel Jesús Jiménez-Navarro and María Martínez-Ballesteros and Isabel Sofia Sousa Brito and Francisco Martínez-Álvarez and Gualberto Asencio-Cortés},

url = {https://doi.org/10.1007/978-3-031-18050-7_54},

doi = {10.1007/978-3-031-18050-7_54},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {N/A},

pages = {557-566},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-40725-3_62,

title = {A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma},

author = {M. Lourdes Linares-Barrera and María Martínez-Ballesteros and José M. García-Heredia and José C. Riquelme},

url = {https://doi.org/10.1007/978-3-031-40725-3_62},

doi = {10.1007/978-3-031-40725-3_62},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {731-742},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.3390/en16052317,

title = {A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia},

author = {Ejigu Tefera Habtemariam and Kula Kekeba and María Martínez-Ballesteros and Francisco Martínez-Álvarez},

url = {https://doi.org/10.3390/en16052317},

doi = {10.3390/en16052317},

year  = {2023},

date = {2023-01-01},

journal = {Energies},

volume = {16},

pages = {2317},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1186/s40537-023-00745-0,

title = {A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting},

author = {Manuel J. Jiménez-Navarro and María Martínez-Ballesteros and Francisco Martínez-Álvarez and Gualberto Asencio-Cortés},

url = {https://doi.org/10.1186/s40537-023-00745-0},

doi = {10.1186/s40537-023-00745-0},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Big Data},

volume = {10},

pages = {N/A},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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{JIMENEZNAVARRO2023815,

title = {PHILNet: A novel efficient approach for time series forecasting using deep learning},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://www.sciencedirect.com/science/article/pii/S0020025523003183},

doi = {https://doi.org/10.1016/j.ins.2023.03.021},

issn = {0020-0255},

year  = {2023},

date = {2023-01-01},

journal = {Information Sciences},

volume = {632},

pages = {815-832},

abstract = {Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful in planning ahead, saving time, resources, and helping avoid undesired scenarios. To make the forecasting, historical data is utilized due to the causal nature of the time series. Several deep learning algorithms have been presented in this area, where the input is processed through a series of non-linear functions to produce the output. We present a novel strategy to improve the performance of deep learning models in time series forecasting in terms of efficiency while reaching similar effectiveness. This approach separates the model into levels, starting with the easiest and continuing to the most difficult. The simpler levels deal with smoothed versions of the input, whereas the most sophisticated level deals with the raw data. This strategy seeks to mimic the human learning process, in which basic tasks are completed initially, followed by more precise and sophisticated ones. Our method achieved promising results, obtaining a 35% improvement in mean squared error and a 2.6 time decrease in training time compared with the best models found in a variety of time series.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-43078-7_2,

title = {Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://doi.org/10.1007/978-3-031-43078-7_2},

doi = {10.1007/978-3-031-43078-7_2},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {15-26},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-42536-3_13,

title = {Explaining Learned Patterns in Deep Learning by Association Rules Mining},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://doi.org/10.1007/978-3-031-42536-3_13},

doi = {10.1007/978-3-031-42536-3_13},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {N/A},

pages = {132-141},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-40725-3_18,

title = {A New Hybrid CNN-LSTM for Wind Power Forecasting in Ethiopia},

author = {E. Tefera and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez},

url = {https://doi.org/10.1007/978-3-031-40725-3_18},

doi = {10.1007/978-3-031-40725-3_18},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {207-218},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-43085-5_50,

title = {Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals},

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

url = {https://doi.org/10.1007/978-3-031-43085-5_50},

doi = {10.1007/978-3-031-43085-5_50},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {626-637},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3555776.3578634,

title = {A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal},

author = {Manuel Jesus Jimenez Navarro and Maria Martinez Ballesteros and Isabel Sofia Brito and Francisco Martinez Alvarez and Gualberto Asencio Cortes},

url = {https://doi.org/10.1145/3555776.3578634},

doi = {10.1145/3555776.3578634},

year  = {2023},

date = {2023-01-01},

journal = {Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing},

volume = {N/A},

pages = {441-448},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3555776.3578994,

title = {Evolutionary computation to explain deep learning models for time series forecasting},

author = {Angela Troncoso Garcia and Alicia Troncoso Lora and Francisco Martinez-Alvarez and Maria Martinez Ballesteros},

url = {https://doi.org/10.1145/3555776.3578994},

doi = {10.1145/3555776.3578994},

year  = {2023},

date = {2023-01-01},

journal = {Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing},

volume = {N/A},

pages = {433-436},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1038/s41392-023-01470-z,

title = {A new treatment for sarcoma extracted from combination of miRNA deregulation and gene association rules},

author = {José Manuel García-Heredia and Marco Pérez and Eva M. Verdugo-Sivianes and María M. Martínez-Ballesteros and Sara M. Ortega-Campos and Amancio Carnero},

url = {https://doi.org/10.1038/s41392-023-01470-z},

doi = {10.1038/s41392-023-01470-z},

year  = {2023},

date = {2023-01-01},

journal = {Signal Transduction and Targeted Therapy},

volume = {8},

pages = {N/A},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3477314.3507343,

title = {A novel approach to discover numerical association based on the coronavirus optimization algorithm},

author = {C. Segarra-Martín and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez},

url = {https://doi.org/10.1145/3477314.3507343},

doi = {10.1145/3477314.3507343},

year  = {2022},

date = {2022-01-01},

journal = {Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing},

volume = {N/A},

pages = {1148-1151},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.procs.2022.09.351,

title = {Explainable machine learning for sleep apnea prediction},

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

url = {https://doi.org/10.1016/j.procs.2022.09.351},

doi = {10.1016/j.procs.2022.09.351},

year  = {2022},

date = {2022-01-01},

journal = {Procedia Computer Science},

volume = {207},

pages = {2930-2939},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.artmed.2020.101976,

title = {Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance},

author = {Laura Macías-García and María Martínez-Ballesteros and José María Luna-Romera and José M. García-Heredia and Jorge García-Gutiérrez and José C. Riquelme-Santos},

url = {https://doi.org/10.1016/j.artmed.2020.101976},

doi = {10.1016/j.artmed.2020.101976},

year  = {2020},

date = {2020-01-01},

journal = {Artificial Intelligence in Medicine},

volume = {110},

pages = {101976},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Luna-Romera2019b,

title = {Analysis of the evolution of the Spanish labour market through unsupervised learning},

author = {J M Luna-Romera and F Núñez-Hernández and M Martínez-Ballesteros and J C Riquelme and C. Usabiaga Ibáñez},

doi = {10.1109/ACCESS.2019.2935386},

year  = {2019},

date = {2019-09-02},

journal = {IEEE Access},

note = {JCR (2018) 4.098},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Luna-Romera2019,

title = {External Clustering Validity Index based on chi-squared statistical test},

author = {J M Luna-Romera and M Martínez-Ballesteros and J García-Gutiérrez and J C Riquelme},

doi = {10.1016/j.ins.2019.02.046},

year  = {2019},

date = {2019-02-18},

journal = {Information Sciences},

note = {JCR(2017): 4,305},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mmgahr18,

title = {MRQAR: a generic MapReduce framework to discover Quantitative Association Rules in Big Data problems},

author = {D Martín and M Martínez-Ballesteros and D García-Gil and J Alcalá-Fdez and F Herrera and J C Riquelme-Santos},

doi = {10.1016/j.knosys.2018.04.037},

year  = {2018},

date = {2018-01-01},

journal = {Knowledge-Based System},

volume = {53},

number = {1},

pages = {176-192},

note = {JCR (2016): 4.529},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mgnr17,

title = {Machine learning techniques to discover genes with Potential Prognosis Role in Alzheimer's Disease using different biological sources},

author = {M Martínez-Ballesteros and J M García-Heredia and I Nepomuceno-Chamorro and J C Riquelme},

doi = {10.1016/j.inffus.2016.11.005},

year  = {2017},

date = {2017-01-01},

journal = {Information Fusion},

volume = {36},

pages = {114-129},

note = {JCR (2015): 4.353, Q1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mmtr16,

title = {Improving a multi-objective evolutionary algorithm to discover quantitative association rules},

author = {M Martínez-Ballesteros and F Martínez-Álvarez and A Troncoso and J C Riquelme},

doi = {10.1007/s10115-015-0911-y},

year  = {2016},

date = {2016-01-01},

journal = {Knowledge and Information Systems},

volume = {49},

number = {2},

pages = {481-509},

note = {JCR (2016): 2.004},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mtmr16,

title = {Obtaining optimal quality measures for quantitative association rules},

author = {M Martínez-Ballesteros and A Troncoso and F Martínez-Álvarez and J C Riquelme},

doi = {10.1016/j.neucom.2014.10.100},

year  = {2016},

date = {2016-01-01},

journal = {Neurocomputing},

volume = {176},

pages = {36-47},

note = {JCR (2016): 3.317, Q1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{l16,

title = {A Study of the Suitability of Autoencoders for Preprocessing Data in Breast Cancer Experimentation},

author = {J M Luna-Romera and L Macías-García and J García-Gutiérrez and M Martínez-Ballesteros and J C Riquelme and R González-Cámpora},

doi = {10.1016/j.jbi.2017.06.020},

year  = {2016},

date = {2016-01-01},

journal = {Journal of Biomedical Informatics},

volume = {72},

number = {C},

pages = {33-44},

note = {JCR (2016): 2.753},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mbtr15,

title = {Enhancing the scalability of evolutionary algorithms to discover quantitative association rules in large-scale datasets},

author = {M Martínez-Ballesteros and J Bacardit and A Troncoso and J C Riquelme},

doi = {10.3233/ICA-140479},

year  = {2015},

date = {2015-01-01},

journal = {Integrated Computer-Aided Engineering},

volume = {22},

number = {1},

pages = {21-39},

note = {JCR (2015): 4.981, Q1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mnr14,

title = {Discovering gene association networks by multi-objective evolutionary quantitative association rules},

author = {M Martínez-Ballesteros and I A Nepomuceno-Chamorro and J C Riquelme},

doi = {10.1016/J.JCSS.2013.03.010},

year  = {2014},

date = {2014-01-01},

journal = {Journal of Computer and Systems Sciences},

volume = {89},

number = {1},

pages = {118-136},

note = {JCR (2014): , Q2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{m14,

title = {Discovering quantitative association rules: A novel approach based on evolutionary algorithms},

author = {M Martínez-Ballesteros},

doi = {10.3233/AIC-130590},

year  = {2014},

date = {2014-01-01},

journal = {AI Communications},

volume = {27},

number = {2},

pages = {153-165},

note = {JCR (2014): 0.547, Q4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mmtj14,

title = {Selecting the best measures to discover quantitative association rules},

author = {M Martínez-Ballesteros and F Martínez-Álvarez and A Troncoso and Riquelme Jc.},

doi = {10.1016/j.neucom.2013.01.056},

year  = {2014},

date = {2014-01-01},

journal = {Neurocomputing},

volume = {126},

pages = {3-14},

note = {JCR (2014): 2.083, Q2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{msrcc11,

title = {Evolutionary Association Rules for Total Ozone Content Modeling from Satellite Observations},

author = {M Martínez-Ballesteros and S Salcedo-Sanz and J C Riquelme and C Casanova-Mateo and J L Camacho},

doi = {10.1016/j.chemolab.2011.09.011},

year  = {2011},

date = {2011-01-01},

journal = {Chemometrics and Intelligent Laboratory Systems},

volume = {109},

number = {2},

pages = {217-227},

note = {JCR (2011): 1.920, Q1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mmr11,

title = {An Evolutionary Algorithm to Discover Quantitative Association Rules in Multidimensional Time Series},

author = {M Martínez-Ballesteros and Troncoso A and Martínez-Álvarez F. and J C Riquelme},

doi = {10.1007/s00500-011-0705-4},

year  = {2011},

date = {2011-01-01},

journal = {Soft Computing},

volume = {15},

number = {10},

pages = {2065-2084},

note = {IF 1.880 24/99 Int App Q1, 30/111 Art Int Q2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mtmj10,

title = {Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution},

author = {M Martínez-Ballesteros and A Troncoso and F Martínez-Álvarez and Riquelme Jc.},

doi = {10.3233/ICA-2010-0340},

year  = {2010},

date = {2010-01-01},

journal = {Integrated Computer-Aided Engineering},

volume = {17},

number = {3},

pages = {227-242},

note = {JCR (2010): 2.122, Q1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}