| Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution M Martínez-Ballesteros, A Troncoso, F Martínez-Álvarez, JC Riquelme Integrated Computer-Aided Engineering 17 (3), 227-242, 2010 | 64 | 2010 |
| An evolutionary algorithm to discover quantitative association rules in multidimensional time series M Martínez-Ballesteros, F Martínez-Álvarez, A Troncoso, JC Riquelme Soft Computing 15 (10), 2065, 2011 | 46 | 2011 |
| Discovering gene association networks by multi-objective evolutionary quantitative association rules M Martínez-Ballesteros, IA Nepomuceno-Chamorro, JC Riquelme Journal of Computer and System Sciences 80 (1), 118-136, 2014 | 35 | 2014 |
| Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets M Martínez-Ballesteros, J Bacardit, A Troncoso, JC Riquelme Integrated Computer-Aided Engineering 22 (1), 21-39, 2015 | 34 | 2015 |
| Selecting the best measures to discover quantitative association rules M Martínez-Ballesteros, F Martínez-Álvarez, A Troncoso, JC Riquelme Neurocomputing, 2013 | 33 | 2013 |
| A nearest neighbours-based algorithm for big time series data forecasting RL Talavera-Llames, R Pérez-Chacón, M Martínez-Ballesteros, ... International Conference on Hybrid Artificial Intelligence Systems, 174-185, 2016 | 25 | 2016 |
| MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems D Martín, M Martínez-Ballesteros, D García-Gil, J Alcalá-Fdez, F Herrera, ... Knowledge-Based Systems 153, 176-192, 2018 | 19 | 2018 |
| An approach to silhouette and dunn clustering indices applied to big data in spark JM Luna-Romera, M del Mar Martínez-Ballesteros, J García-Gutiérrez, ... Conference of the Spanish Association for Artificial Intelligence, 160-169, 2016 | 19 | 2016 |
| An approach to validity indices for clustering techniques in big data JM Luna-Romera, J García-Gutiérrez, M Martínez-Ballesteros, ... Progress in Artificial Intelligence 7 (2), 81-94, 2018 | 18 | 2018 |
| A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation L Macías-García, JM Luna-Romera, J García-Gutiérrez, ... Journal of biomedical informatics 72, 33-44, 2017 | 17 | 2017 |
| Evolutionary association rules for total ozone content modeling from satellite observations M Martínez-Ballesteros, S Salcedo-Sanz, JC Riquelme, ... Chemometrics and Intelligent Laboratory Systems 109 (2), 217-227, 2011 | 15 | 2011 |
| Quantitative association rules applied to climatological time series forecasting M Martínez-Ballesteros, F Martínez-Álvarez, A Troncoso, JC Riquelme International Conference on Intelligent Data Engineering and Automated …, 2009 | 14 | 2009 |
| Obtaining optimal quality measures for quantitative association rules M Martínez-Ballesteros, A Troncoso, F Martínez-Álvarez, JC Riquelme Neurocomputing 176, 36-47, 2016 | 13 | 2016 |
| Analysis of measures of quantitative association rules M Martínez-Ballesteros, JC Riquelme International Conference on Hybrid Artificial Intelligence Systems, 319-326, 2011 | 13 | 2011 |
| Machine learning techniques to discover genes with potential prognosis role in Alzheimer’s disease using different biological sources M Martínez-Ballesteros, JM García-Heredia, IA Nepomuceno-Chamorro, ... Information Fusion 36, 114-129, 2017 | 12 | 2017 |
| Improving a multi-objective evolutionary algorithm to discover quantitative association rules M Martínez-Ballesteros, A Troncoso, F Martínez-Álvarez, JC Riquelme Knowledge and Information Systems 49 (2), 481-509, 2016 | 11 | 2016 |
| External clustering validity index based on chi-squared statistical test JM Luna-Romera, M Martínez-Ballesteros, J García-Gutiérrez, ... Information Sciences 487, 1-17, 2019 | 7 | 2019 |
| Inferring gene-gene associations from Quantitative Association Rules M Martínez-Ballesteros, I Nepomuceno-Chamorro, JC Riquelme 11th International Conference on Intelligent Systems Design and Applications …, 2011 | 6 | 2011 |
| MOPNAR-BigData: un diseno MapReduce para la extracción de reglas de asociación cuantitativas en problemas de Big Data D Martín, M Martínez-Ballesteros, S Río, J Alcalá-Fdez, J Riquelme, ... Actas de la XVI Conferencia de la Asociación Española para la Inteligencia …, 2015 | 3 | 2015 |
| Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance L Macías-García, M Martínez-Ballesteros, JM Luna-Romera, ... Artificial Intelligence in Medicine 110, 101976, 2020 | 2 | 2020 |
José C. RiquelmeUniversidad de SevillaBestätigte E-Mail-Adresse bei us.es
