Quantitative Association Rules Applied to Climatological Time Series Forecasting. - María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso Lora; José C. Riquelme (IDEAL, 2009)

Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. - María Martínez-Ballesteros; Alicia Troncoso Lora; Francisco Martínez-Álvarez; José C. Riquelme (Integr. Comput. Aided Eng., 2010)

Analysis of Measures of Quantitative Association Rules. - María Martínez-Ballesteros; José Cristóbal Riquelme Santos (HAIS (2), 2011)

Mining Quantitative Association Rules in Microarray Data using Evolutive Algorithms. - María Martínez-Ballesteros; Cristina Rubio-Escudero; José C. Riquelme; Francisco Martínez-Álvarez (ICAART (1), 2011)

Inferring gene-gene associations from Quantitative Association Rules. - María Martínez-Ballesteros; Isabel A. Nepomuceno-Chamorro; José C. Riquelme (ISDA, 2011)

On the use of algorithms to discover motifs in DNA sequences. - Cristina Rubio-Escudero; Francisco Martínez-Álvarez; María Martínez-Ballesteros; José C. Riquelme (ISDA, 2011)

An evolutionary algorithm to discover quantitative association rules in multidimensional time series. - María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso Lora; José C. Riquelme (Soft Comput., 2011)

ra A Sensitivity Analysis for Quality Measures of Quantitative Association Rules. - María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso Lora; José C. Riquelme (HAIS, 2013)

Selecting the best measures to discover quantitative association rules. - María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso Lora; José C. Riquelme (Neurocomputing, 2014)

Discovering gene association networks by multi-objective evolutionary quantitative association rules. - María Martínez-Ballesteros; Isabel A. Nepomuceno-Chamorro; José C. Riquelme (J. Comput. Syst. Sci., 2014)

Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets. - María Martínez-Ballesteros; Jaume Bacardit; Alicia Troncoso Lora; José C. Riquelme (Integr. Comput. Aided Eng., 2015)

An Approach to Silhouette and Dunn Clustering Indices Applied to Big Data in Spark. - José María Luna-Romera; María del Mar Martínez-Ballesteros; Jorge García-Gutiérrez; José Cristóbal Riquelme Santos (CAEPIA, 2016)

Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules. - Alejandro Sánchez Medina; Alberto Gil Pichardo; José Manuel García-Heredia; María Martínez-Ballesteros (HAIS, 2016)

A Nearest Neighbours-Based Algorithm for Big Time Series Data Forecasting. - Ricardo L. Talavera-Llames; Rubén Pérez-Chacón; María Martínez-Ballesteros; Alicia Troncoso; Francisco Martínez-Álvarez (HAIS, 2016)

Obtaining optimal quality measures for quantitative association rules. - María Martínez-Ballesteros; Alicia Troncoso; Francisco Martínez-Álvarez; José C. Riquelme (Neurocomputing, 2016)

Improving a multi-objective evolutionary algorithm to discover quantitative association rules. - María Martínez-Ballesteros; Alicia Troncoso Lora; Francisco Martínez-Álvarez; José C. Riquelme (Knowl. Inf. Syst., 2016)

Applications of Computational Intelligence in Time Series. - Francisco Martínez-Álvarez; Alicia Troncoso; Jorge Reyes; María Martínez-Ballesteros; José C. Riquelme (Comput. Intell. Neurosci., 2017)

Machine learning techniques to discover genes with potential prognosis role in Alzheimer's disease using different biological sources. - María Martínez-Ballesteros; José Manuel García-Heredia; Isabel A. Nepomuceno-Chamorro; José Cristóbal Riquelme Santos (Inf. Fusion, 2017)

A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation. - Laura Macías-García; José María Luna-Romera; Jorge García-Gutiérrez; María Martínez-Ballesteros; José Cristóbal Riquelme Santos; Ricardo González-Cámpora (J. Biomed. Informatics, 2017)

MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems. - Diana Martín; María Martínez-Ballesteros; Diego García-Gil; Jesús Alcalá-Fdez; Francisco Herrera; José Cristóbal Riquelme Santos (Knowl. Based Syst., 2018)

An approach to validity indices for clustering techniques in Big Data. - José María Luna-Romera; Jorge García-Gutiérrez; María Martínez-Ballesteros; José Cristóbal Riquelme Santos (Prog. Artif. Intell., 2018)

Analysis of the Evolution of the Spanish Labour Market Through Unsupervised Learning. - José María Luna-Romera; Fernando Núñez-Hernández; María Martínez-Ballesteros; José C. Riquelme; Carlos Usabiaga (IEEE Access, 2019)

External clustering validity index based on chi-squared statistical test. - José María Luna-Romera; María Martínez-Ballesteros; Jorge García-Gutiérrez; José C. Riquelme (Inf. Sci., 2019)

Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance. - Laura Macías-García; María Martínez-Ballesteros; José María Luna-Romera; José Manuel García-Heredia; Jorge García-Gutiérrez; José Cristóbal Riquelme Santos (Artif. Intell. Medicine, 2020)

Spanish adaptation and validation of the User Version of the Mobile Application Rating Scale (uMARS). - Rubén Martín Payo; Sergio Carrasco-Santos; Marcelino Cuesta; Stoyan R. Stoyanov; Xana Gonzalez-Mendez; María del Mar Martínez-Ballesteros (J. Am. Medical Informatics Assoc., 2021)

Explainable machine learning for sleep apnea prediction. - A. R. Troncoso-García; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso (KES, 2022)

A novel approach to discover numerical association based on the coronavirus optimization algorithm. - C. Segarra-Martín; María Martínez-Ballesteros; Alicia Troncoso; Francisco Martínez-Álvarez (SAC, 2022)

Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection. - Manuel Jesús Jiménez-Navarro; María Martínez-Ballesteros; Isabel Sofia Sousa Brito; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (SOCO, 2022)

Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation. - Manuel Jesús Jiménez-Navarro; Belén Vega-Márquez; José María Luna-Romera; Manuel Carranza-García; María Martínez-Ballesteros (CISIS-ICEUTE, 2023)

A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma. - Maria Lourdes Linares-Barrera; María Martínez-Ballesteros; José Manuel García-Heredia; José C. Riquelme (HAIS, 2023)

A New Hybrid CNN-LSTM for Wind Power Forecasting in Ethiopia. - E. Tefera; María Martínez-Ballesteros; Alicia Troncoso; Francisco Martínez-Álvarez (HAIS, 2023)

Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning. - M. J. Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (IWANN (2), 2023)

Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals. - A. R. Troncoso-García; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso Lora (IWANN (1), 2023)

A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal. - Manuel Jesús Jiménez-Navarro; María Martínez-Ballesteros; Isabel Sofia Brito; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (SAC, 2023)

Evolutionary computation to explain deep learning models for time series forecasting. - Angela Troncoso-García; Alicia Troncoso Lora; Francisco Martínez-Álvarez; María Martínez-Ballesteros (SAC, 2023)

Explaining Learned Patterns in Deep Learning by Association Rules Mining. - M. J. Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (SOCO (2), 2023)

PHILNet: A novel efficient approach for time series forecasting using deep learning. - M. J. Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (Inf. Sci., 2023)

A new approach based on association rules to add explainability to time series forecasting models. - A. R. Troncoso-García; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso (Inf. Fusion, 2023)

A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting. - Manuel Jesús Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (J. Big Data, 2023)

Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting. - Maria Lourdes Linares-Barrera; Manuel Jesús Jiménez-Navarro; José C. Riquelme; María Martínez-Ballesteros (CAEPIA, 2024)

Evolutionary Feature Selection for Time-Series Forecasting. - Maria Lourdes Linares-Barrera; Manuel Jesús Jiménez-Navarro; Isabel Sofia Brito; José C. Riquelme; María Martínez-Ballesteros (SAC, 2024)

Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection. - A. R. Troncoso-García; M. J. Jiménez-Navarro; Maria Lourdes Linares-Barrera; Isabel Sofia Brito; Francisco Martínez-Álvarez; María Martínez-Ballesteros (SOCO (1), 2024)

Explaining deep learning models for ozone pollution prediction via embedded feature selection. - M. J. Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (Appl. Soft Comput., 2024)

From simple to complex: a sequential method for enhancing time series forecasting with deep learning. - M. J. Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso; Gualberto Asencio-Cortés (Log. J. IGPL, 2024)

Bioinspired evolutionary metaheuristic based on COVID spread for discovering numerical association rules. - Cristina Herruzo-Lodeiro; Francesc Rodríguez-Díaz; Alicia Troncoso; María Martínez-Ballesteros (SAC, 2025)

A novel approach based on clustering and optimized ensemble deep learning for energy consumption forecasting in Ethiopia. - E. Tefera Habtemariam; María Martínez-Ballesteros; Alicia Troncoso; Francisco Martínez-Álvarez (Neurocomputing, 2025)

A New Metric Based on Association Rules to Assess Feature-Attribution Explainability Techniques for Time Series Forecasting. - Angela del Robledo Troncoso-García; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso (IEEE Trans. Pattern Anal. Mach. Intell., 2025)