Jiménez, Manuel Jesús;M. Martínez-Ballesteros;Martínez-Álvarez, Francisco;Troncoso-Lora, Alicia;Asencio-Cortes, Gualberto:
From Simple to Complex: A Sequential Method for Enhancing Time Series Forecasting with Deep Learning. Interest Group in Pure and Applied Logics. Logic Journal. 2023. Vol: In press.
Troncoso, Angela Del Robledo;M. Martínez-Ballesteros;Martínez-Álvarez, Francisco;Troncoso-Lora, Alicia:
A new approach based on association rules to add explainability to time series forecasting models. Information Fusion. 2023. Vol: 94. Pág. 169-180. 10.1016/j.inffus.2023.01.021.
Jiménez, Manuel Jesús;M. Martínez-Ballesteros;Martínez-Álvarez, Francisco;Asencio-Cortes, Gualberto:
PHILNet: A novel efficient approach for time series forecasting using deep learning. Information Sciences. 2023. Vol: 632. Pág. 815-832. 10.1016/j.ins.2023.03.021.
Tefera-habtemariam, Ejigu;Kekeba, Kula;M. Martínez-Ballesteros;Martínez-Álvarez, Francisco:
A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia. Energies. 2023. Vol: 16. Núm: 5. Pág. 1-22. 10.3390/en16052317.
Jiménez, Manuel Jesús;M. Martínez-Ballesteros;Martínez-Álvarez, Francisco;Asencio-Cortes, Gualberto:
A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting. Journal of Big Data. 2023. Vol: 10. Núm: 80. Pág. 1-19. https://doi.org/10.1186/s40537-023-00745-0.
Garcia-Heredia, Jose Manuel;M. Martínez-Ballesteros:
A new treatment for sarcoma extracted from combination of miRNA deregulation and gene association rules. Signal Transduction and Targeted Therapy. 2023. Vol: 8. Núm: 231. https://doi.org/10.1038/s41392-023-01470-z.
Troncoso, Angela Del Robledo;M. Martínez-Ballesteros;Martínez-Álvarez, Francisco;Troncoso-Lora, Alicia:
Explainable machine learning for sleep apnea prediction. Procedia Computer Science. 2022. Vol: 207. Pág. 2930-2939. https://doi.org/10.1016/j.procs.2022.09.351.
Macías-García, Laura;M. Martínez-Ballesteros;Luna, José María;Garcia-Heredia, Jose Manuel;García-Gutiérrez, Jorge;Riquelme-Santos, José Cristóbal:
Autoencoded DNA Methylation Data to Predict Breast Cancer Recurrence: Machine Learning Models and Gene-Weight Significance. Artificial Intelligence in Medicine. 2020. https://doi.org/10.1016/j.artmed.2020.101976.
Luna, José María;M. Martínez-Ballesteros;García-Gutiérrez, Jorge;Riquelme-Santos, José Cristóbal:
External clustering validity index based on chi-squared statistical test. Information Sciences. 2019. Vol: 487. Pág. 1-17. 10.1016/j.ins.2019.02.046.
Luna, José María;Nuñez-Hernandez, Fernando;M. Martínez-Ballesteros;Riquelme-Santos, José Cristóbal;Usabiaga-Ibañez, Carlos:
Analysis of the Evolution of the Spanish Labour Market through Unsupervised Learning. IEEE Access. 2019. Vol: 7. Pág. 121695-121708. 10.1109/ACCESS.2019.2935386.
Luna, José María;García-Gutiérrez, Jorge;M. Martínez-Ballesteros;Riquelme-Santos, José Cristóbal:
An approach to validity indices for clustering techniques in Big Data. Progress in Artificial Intelligence. 2018. Vol: 7. Núm: 2. Pág. 81-94. 10.1007/s13748-017-0135-3.
MARTIN-RODRIGUEZ, DIANA;M. Martínez-Ballesteros;García-Gil, Diego;Alcalá-Fernández, Jesús;Riquelme-Santos, José Cristóbal;Herrera-Triguero, Francisco:
MRQAR: a generic MapReduce framework to discover Quantitative Association Rules in Big Data problems. Knowledge-Based Systems. 2018. Vol: 153. Pág. 176-192. 10.1016/j.knosys.2018.04.037.
M. Martínez-Ballesteros;Garcia-Heredia, Jose Manuel;Nepomuceno-Chamorro, Isabel De Los Angeles;Riquelme-Santos, José Cristóbal:
Machine learning techniques to discover genes with potential prognosis role in Alzheimer¿s disease using different biological sources. Information Fusion. 2017. Vol: 36. Pág. 114-129. 10.1016/j.inffus.2016.11.005.
Martínez-Álvarez, Francisco;Troncoso-Lora, Alicia;M. Martínez-Ballesteros;Riquelme-Santos, José Cristóbal:
Applications of Computational Intelligence in Time Series. Computational Intelligence and Neuroscience. 2017. Vol: 2017. Pág. 1-2. https://doi.org/10.1155/2017/9361749.
Macías-García, Laura;Luna, José María;García-Gutiérrez, Jorge;M. Martínez-Ballesteros;Riquelme-Santos, José Cristóbal;González-Campora, Ricardo:
A Study of the Suitability of Autoencoders for Preprocessing Data in Breast Cancer Experimentation. Journal of Biomedical Informatics. 2017. Vol: 72. Pág. 33-44. 10.1016/j.jbi.2017.06.020.
Sánchez-medina, Alejandro;Gil-pichardo, Alberto;Garcia-Heredia, Jose Manuel;M. Martínez-Ballesteros:
Discovery of Genes implied in Cancer by Genetic Algorithms and Association Rules. Lecture Notes in Computer Science. 2016. Vol: 9846. Pág. 694-705. 10.1007/978-3-319-32034-2_58.
M. Martínez-Ballesteros;Troncoso-Lora, Alicia;Martínez-Álvarez, Francisco;Riquelme-Santos, José Cristóbal:
Obtaining optimal quality measures for quantitative association rules. Neurocomputing. 2016. Vol: 176. Pág. 36-47. 10.1016/j.neucom.2014.10.100.
Talavera, Ricardo;Pérez , Rubén ;M. Martínez-Ballesteros;Troncoso-Lora, Alicia;Martínez-Álvarez, Francisco:
A nearest neighbours-based algorithm for big time series data forecasting. Lecture Notes in Computer Science. 2016. Vol: 9846. Pág. 174-185. 10.1007/978-3-319-32034-2_15.
Luna, José María;M. Martínez-Ballesteros;García-Gutiérrez, Jorge;Riquelme-Santos, José Cristóbal:
An Approach to Silhouette and Dunn Clustering Indices Applied to Big Data in Spark. Lecture Notes in Computer Science. 2016. Vol: 9868. Pág. 160-169. 10.1007/978-3-319-44636-3_15.
M. Martínez-Ballesteros;Troncoso-Lora, Alicia;Martínez-Álvarez, Francisco;Riquelme-Santos, José Cristóbal:
Improving a multi-objective evolutionary algorithm to discover quantitative association rule. Knowledge and Information Systems. 2016. Vol: 49. Núm: 2. Pág. 481-509. 10.1007/s10115-015-0911-y.
M. Martínez-Ballesteros;Bacardit-Peñarroya, Jaume;Troncoso-Lora, Alicia;Riquelme-Santos, José Cristóbal:
Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets. Integrated Computer-Aided Engineering. 2015. Vol: 22. Núm: 1. Pág. 21-39. 10.3233/ICA-140479.
M. Martínez-Ballesteros;Nepomuceno-Chamorro, Isabel De Los Angeles;Riquelme-Santos, José Cristóbal:
Discovering gene association networks by multi-objective evolutionary quantitative association rules. Journal of Computer and System Sciences. 2014. Vol: 80. Núm: 1. Pág. 118-136. j.jcss.2013.03.010.
M. Martínez-Ballesteros;Martínez-Álvarez, Francisco;Troncoso-Lora, Alicia;Riquelme-Santos, José Cristóbal:
Selecting the best measures to discover quantitative association rules. Neurocomputing. 2014. Vol: 126. Núm: 27. Pág. 3-14. 10.1016/J.NEUCOM.2013.01.05 6.
M. Martínez-Ballesteros:
Discovering quantitative association rules: A novel approach based on evolutionary algorithms. AI communications. 2014. Vol: 27. Núm: 2. Pág. 153-165. 10.3233/AIC-130590.
M. Martínez-Ballesteros;Martínez-Álvarez, Francisco;Troncoso-Lora, Alicia;Riquelme-Santos, José Cristóbal:
A sensitivity analysis for quality measures of association rules. Lecture Notes in Computer Science. 2013. Vol: 8073. Pág. 578-587. link.springer.com/chapter/10.1007%2F978-3-642-40846-5_58#.
Martínez-Gasca, Rafael;Álvarez-De La Concepción, Miguel Ángel;Soria-Morillo, Luis Miguel;Parody-Núñez, Maria Luisa;M. Martínez-Ballesteros;Jiménez-Ramírez, Andrés:
Extensiones para el Ciclo de Mejora Continua en la enseñanza e investigación de Ingeniería Informática. Revista de Enseñanza Universitaria. 2011. Vol: 1. Núm: 38. Pág. 4-26.
M. Martínez-Ballesteros;Martínez-Álvarez, Francisco;Troncoso-Lora, Alicia;Riquelme-Santos, José Cristóbal:
An Evolutionary Algorithm to Discover Quantitative Association Rules in Multidimensional Time Series. Soft Computing. 2011. Vol: 15. Núm: 10. Pág. 2065-2084. 10.1007/s00500-011-0705-4.
M. Martínez-Ballesteros;Salcedo-Sanz, Sancho;Riquelme-Santos, José Cristóbal;Casanova-mateo, C.;Camacho, J. L. :
Evolutionary association rules for total ozone content modeling from satellite observations. Chemometrics and Intelligent Laboratory Systems. 2011. Vol: 109. Núm: 2. Pág. 217-227. 10.1016/j.chemolab.2011.09.011,.
M. Martínez-Ballesteros;Riquelme-Santos, José Cristóbal:
Analysis of Measures of Quantitative Association Rules. Lecture Notes in Computer Science. 2011. Vol: 6679. Núm: PART 2. Pág. 319-326. 10.1007/978-3-642-21222-2_39.
M. Martínez-Ballesteros;Martínez-Álvarez, Francisco;Troncoso-Lora, Alicia;Riquelme-Santos, José Cristóbal:
Mining quantitative association rules based on evoluationary computation and its application to atmospheric pollution. Integrated Computer-Aided Engineering. 2010. Vol: 17. Núm: 3. Pág. 227-242. 10.3233/ICA-2010-0340.
M. Martínez-Ballesteros;Martínez-Álvarez, Francisco;Troncoso-Lora, Alicia;Riquelme-Santos, José Cristóbal:
Quantitative association rules applied to climatological time series forecasting. Lecture Notes in Computer Science. 2009. Vol: 5788. Pág. 284-291. 10.1007/978-3-642-04394-9_35.