| 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 | 79 | 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, 2065-2084, 2011 | 61 | 2011 |
| 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 | 56 | 2019 |
| 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 | 45 | 2018 |
| A nearest neighbours-based algorithm for big time series data forecasting RL Talavera-Llames, R Pérez-Chacón, M Martínez-Ballesteros, ... Hybrid Artificial Intelligent Systems: 11th International Conference, HAIS …, 2016 | 44 | 2016 |
| A new approach based on association rules to add explainability to time series forecasting models AR Troncoso-García, M Martínez-Ballesteros, F Martínez-Álvarez, ... Information Fusion 94, 169-180, 2023 | 43 | 2023 |
| 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 | 42 | 2015 |
| Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance L Macías-García, M Martinez-Ballesteros, JM Luna-Romera, ... Artificial Intelligence in Medicine 110, 101976, 2020 | 41 | 2020 |
| Selecting the best measures to discover quantitative association rules M Martínez-Ballesteros, F Martínez-Álvarez, A Troncoso, JC Riquelme Neurocomputing, 2013 | 41 | 2013 |
| 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 | 38 | 2014 |
| 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, 81-94, 2018 | 35 | 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 | 35 | 2017 |
| An approach to silhouette and dunn clustering indices applied to big data in spark JM Luna-Romera, M del Mar Martinez-Ballesteros, J Garcia-Gutierrez, ... Advances in Artificial Intelligence: 17th Conference of the Spanish …, 2016 | 33 | 2016 |
| 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 | 30 | 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, 481-509, 2016 | 29 | 2016 |
| A Bayesian optimization-based LSTM model for wind power forecasting in the Adama district, Ethiopia ET Habtemariam, K Kekeba, M Martínez-Ballesteros, F Martínez-Álvarez Energies 16 (5), 2317, 2023 | 24 | 2023 |
| Explainable machine learning for sleep apnea prediction AR Troncoso-García, M Martínez-Ballesteros, F Martínez-Álvarez, ... Procedia Computer Science 207, 2930-2939, 2022 | 21 | 2022 |
| Analysis of measures of quantitative association rules M Martínez-Ballesteros, JC Riquelme Hybrid Artificial Intelligent Systems: 6th International Conference, HAIS …, 2011 | 17 | 2011 |
| Quantitative association rules applied to climatological time series forecasting M Martínez-Ballesteros, F Martínez-Álvarez, A Troncoso, JC Riquelme Intelligent Data Engineering and Automated Learning-IDEAL 2009: 10th …, 2009 | 17 | 2009 |
| Obtaining optimal quality measures for quantitative association rules M Martínez-Ballesteros, A Troncoso, F Martínez-Álvarez, JC Riquelme Neurocomputing 176, 36-47, 2016 | 16 | 2016 |
José C. RiquelmeUniversidad de SevillaVerified email at us.es
