| Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring J Abellán, CJ Mantas Expert Systems with Applications 41 (8), 3825-3830, 2014 | 213 | 2014 |
| Credal-C4. 5: Decision tree based on imprecise probabilities to classify noisy data CJ Mantas, J Abellan Expert Systems with Applications 41 (10), 4625-4637, 2014 | 182 | 2014 |
| Interpretation of artificial neural networks by means of fuzzy rules JL Castro, CJ Mantas, JM Benítez IEEE Transactions on Neural Networks 13 (1), 101-116, 2002 | 156 | 2002 |
| Neural networks with a continuous squashing function in the output are universal approximators JL Castro, CJ Mantas, JM Benıtez Neural Networks 13 (6), 561-563, 2000 | 95 | 2000 |
| Extraction of fuzzy rules from support vector machines JL Castro, LD Flores-Hidalgo, CJ Mantas, JM Puche Fuzzy Sets and Systems 158 (18), 2057-2077, 2007 | 80 | 2007 |
| A comparison of random forest based algorithms: random credal random forest versus oblique random forest CJ Mantas, JG Castellano, S Moral-García, J Abellán Soft Computing 23, 10739-10754, 2019 | 79 | 2019 |
| A random forest approach using imprecise probabilities J Abellán, CJ Mantas, JG Castellano Knowledge-Based Systems 134, 72-84, 2017 | 74 | 2017 |
| Extraction of similarity based fuzzy rules from artificial neural networks CJ Mantas, JM Puche, JM Mantas International Journal of Approximate Reasoning 43 (2), 202-221, 2006 | 55 | 2006 |
| Analysis of Credal-C4. 5 for classification in noisy domains CJ Mantas, J Abellán, JG Castellano Expert Systems with Applications 61, 314-326, 2016 | 53 | 2016 |
| Increasing diversity in random forest learning algorithm via imprecise probabilities J Abellan, CJ Mantas, JG Castellano, S Moral-García Expert Systems with Applications 97, 228-243, 2018 | 51 | 2018 |
| Analysis and extension of decision trees based on imprecise probabilities: Application on noisy data CJ Mantas, J Abellán Expert Systems with Applications 41 (5), 2514-2525, 2014 | 51 | 2014 |
| Bagging of credal decision trees for imprecise classification S Moral-García, CJ Mantas, JG Castellano, MD Benítez, J Abellan Expert Systems with Applications 141, 112944, 2020 | 46 | 2020 |
| Artificial neural networks are zero-order TSK fuzzy systems CJ Mantas, JÉM Puche IEEE Transactions on Fuzzy Systems 16 (3), 630-643, 2008 | 43 | 2008 |
| Decision Tree Ensemble Method for Analyzing Traffic Accidents of Novice Drivers in Urban Areas S Moral-García, JG Castellano, CJ Mantas, A Montella, J Abellán Entropy 21 (4), 360, 2019 | 42 | 2019 |
| AdaptativeCC4. 5: Credal C4. 5 with a rough class noise estimator J Abellán, CJ Mantas, JG Castellano Expert Systems with Applications 92, 363-379, 2018 | 26 | 2018 |
| Ensemble of classifier chains and Credal C4. 5 for solving multi-label classification S Moral-García, CJ Mantas, JG Castellano, J Abellán Progress in Artificial Intelligence 8 (2), 195-213, 2019 | 18 | 2019 |
| A neuro-fuzzy approach for feature selection JM Benitez, JL Castro, CJ Mantas, F Rojas Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International …, 2001 | 13 | 2001 |
| A generic fuzzy aggregation operator: rules extraction from and insertion into artificial neural networks CJ Mantas Soft Computing 12 (5), 493-514, 2008 | 12 | 2008 |
| Credal decision trees to classify noisy data sets CJ Mantas, J Abellán International Conference on Hybrid Artificial Intelligence Systems, 689-696, 2014 | 11 | 2014 |
| Non-parametric predictive inference for solving multi-label classification S Moral-García, CJ Mantas, JG Castellano, J Abellán Applied Soft Computing 88, 106011, 2020 | 10 | 2020 |
