María Martínez-Ballesteros

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 29

Top Topics

Association Rules

Clustering Validity

Top Venues

Publications

Maria Lourdes Linares-Barrera, Manuel Jesús Jiménez-Navarro, Isabel Sofia Brito, José C. Riquelme, María Martínez-Ballesteros
Evolutionary Feature Selection for Time-Series Forecasting. SAC (2024)
Maria Lourdes Linares-Barrera, Manuel Jesús Jiménez-Navarro, José C. Riquelme, María Martínez-Ballesteros
Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting. CAEPIA (2024)
M. J. Jiménez-Navarro, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés
Explaining deep learning models for ozone pollution prediction via embedded feature selection. Appl. Soft Comput. 157 (2024)
M. J. Jiménez-Navarro, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés
Explaining Learned Patterns in Deep Learning by Association Rules Mining. SOCO (2) (2023)
Manuel Jesús Jiménez-Navarro, Belén Vega-Márquez, José María Luna-Romera, Manuel Carranza-García, María Martínez-Ballesteros
Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation. CISIS-ICEUTE (2023)
Angela Troncoso-García, Alicia Troncoso Lora, Francisco Martínez-Álvarez, María Martínez-Ballesteros
Evolutionary computation to explain deep learning models for time series forecasting. SAC (2023)
M. J. Jiménez-Navarro, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés
Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning. IWANN (2) (2023)
Manuel Jesús Jiménez-Navarro, María Martínez-Ballesteros, Isabel Sofia Brito, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés
A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal. SAC (2023)
Manuel Jesús Jiménez-Navarro, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés
A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting. J. Big Data 10 (1) (2023)
E. Tefera, María Martínez-Ballesteros, Alicia Troncoso, Francisco Martínez-Álvarez
A New Hybrid CNN-LSTM for Wind Power Forecasting in Ethiopia. HAIS (2023)
M. J. Jiménez-Navarro, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés
PHILNet: A novel efficient approach for time series forecasting using deep learning. Inf. Sci. 632 (2023)
A. R. Troncoso-García, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso
A new approach based on association rules to add explainability to time series forecasting models. Inf. Fusion 94 (2023)
M. Lourdes Linares-Barrera, María Martínez-Ballesteros, José Manuel García-Heredia, José C. Riquelme
A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma. HAIS (2023)
A. R. Troncoso-García, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso
Explainable machine learning for sleep apnea prediction. KES (2022)
C. Segarra-Martín, María Martínez-Ballesteros, Alicia Troncoso, Francisco Martínez-Álvarez
A novel approach to discover numerical association based on the coronavirus optimization algorithm. SAC (2022)
Manuel Jesús Jiménez-Navarro, María Martínez-Ballesteros, Isabel Sofia Sousa Brito, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés
Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection. SOCO (2022)
Laura Macías-García, María Martínez-Ballesteros, José María Luna-Romera, José Manuel García-Heredia, Jorge García-Gutiérrez, José Cristóbal Riquelme Santos
Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance. Artif. Intell. Medicine 110 (2020)
José María Luna-Romera, María Martínez-Ballesteros, Jorge García-Gutiérrez, José C. Riquelme
External clustering validity index based on chi-squared statistical test. Inf. Sci. 487 (2019)
José María Luna-Romera, Fernando Núñez-Hernández, María Martínez-Ballesteros, José C. Riquelme, Carlos Usabiaga
Analysis of the Evolution of the Spanish Labour Market Through Unsupervised Learning. IEEE Access 7 (2019)
Diana Martín, María Martínez-Ballesteros, Diego García-Gil, Jesús Alcalá-Fdez, Francisco Herrera, José Cristóbal Riquelme Santos
MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems. Knowl. Based Syst. 153 (2018)
José María Luna-Romera, Jorge García-Gutiérrez, María Martínez-Ballesteros, José Cristóbal Riquelme Santos
An approach to validity indices for clustering techniques in Big Data. Prog. Artif. Intell. 7 (2) (2018)
María Martínez-Ballesteros, José Manuel García-Heredia, Isabel A. Nepomuceno-Chamorro, José Cristóbal Riquelme Santos
Machine learning techniques to discover genes with potential prognosis role in Alzheimer's disease using different biological sources. Inf. Fusion 36 (2017)
Laura Macías-García, José María Luna-Romera, Jorge García-Gutiérrez, María Martínez-Ballesteros, José Cristóbal Riquelme Santos, Ricardo González-Cámpora
A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation. J. Biomed. Informatics 72 (2017)
Francisco Martínez-Álvarez, Alicia Troncoso, Jorge Reyes, María Martínez-Ballesteros, José C. Riquelme
Applications of Computational Intelligence in Time Series. Comput. Intell. Neurosci. 2017 (2017)
Ricardo L. Talavera-Llames, Rubén Pérez-Chacón, María Martínez-Ballesteros, Alicia Troncoso, Francisco Martínez-Álvarez
A Nearest Neighbours-Based Algorithm for Big Time Series Data Forecasting. HAIS (2016)
María Martínez-Ballesteros, Alicia Troncoso, Francisco Martínez-Álvarez, José C. Riquelme
Obtaining optimal quality measures for quantitative association rules. Neurocomputing 176 (2016)
Alejandro Sánchez Medina, Alberto Gil Pichardo, José Manuel García-Heredia, María Martínez-Ballesteros
Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules. HAIS (2016)
María Martínez-Ballesteros, Alicia Troncoso Lora, Francisco Martínez-Álvarez, José C. Riquelme
Improving a multi-objective evolutionary algorithm to discover quantitative association rules. Knowl. Inf. Syst. 49 (2) (2016)
María Martínez-Ballesteros, Jaume Bacardit, Alicia Troncoso Lora, José C. Riquelme
Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets. Integr. Comput. Aided Eng. 22 (1) (2015)
María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme
Selecting the best measures to discover quantitative association rules. Neurocomputing 126 (2014)
María Martínez-Ballesteros, Isabel A. Nepomuceno-Chamorro, José C. Riquelme
Discovering gene association networks by multi-objective evolutionary quantitative association rules. J. Comput. Syst. Sci. 80 (1) (2014)
María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme
ra A Sensitivity Analysis for Quality Measures of Quantitative Association Rules. HAIS (2013)
María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme
An evolutionary algorithm to discover quantitative association rules in multidimensional time series. Soft Comput. 15 (10) (2011)
Cristina Rubio-Escudero, Francisco Martínez-Álvarez, María Martínez-Ballesteros, José C. Riquelme
On the use of algorithms to discover motifs in DNA sequences. ISDA (2011)
María Martínez-Ballesteros, José Cristóbal Riquelme Santos
Analysis of Measures of Quantitative Association Rules. HAIS (2) (2011)
María Martínez-Ballesteros, Isabel A. Nepomuceno-Chamorro, José C. Riquelme
Inferring gene-gene associations from Quantitative Association Rules. ISDA (2011)
María Martínez-Ballesteros, Cristina Rubio-Escudero, José C. Riquelme, Francisco Martínez-Álvarez
Mining Quantitative Association Rules in Microarray Data using Evolutive Algorithms. ICAART (1) (2011)
María Martínez-Ballesteros, Alicia Troncoso Lora, Francisco Martínez-Álvarez, José C. Riquelme
Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integr. Comput. Aided Eng. 17 (3) (2010)
María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme
Quantitative Association Rules Applied to Climatological Time Series Forecasting. IDEAL (2009)