| Binary relevance efficacy for multilabel classification O Luaces, J Díez, J Barranquero, JJ del Coz, A Bahamonde Progress in Artificial Intelligence 1, 303-313, 2012 | 330 | 2012 |
| Dependent binary relevance models for multi-label classification E Montanes, R Senge, J Barranquero, JR Quevedo, JJ del Coz, ... Pattern Recognition 47 (3), 1494-1508, 2014 | 175 | 2014 |
| A review on quantification learning P González, A Castaño, NV Chawla, JJD Coz ACM Computing Surveys (CSUR) 50 (5), 1-40, 2017 | 156 | 2017 |
| The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry F Goyache, A Bahamonde, J Alonso, S López, JJ Del Coz, JR Quevedo, ... Trends in Food Science & Technology 12 (10), 370-381, 2001 | 111 | 2001 |
| Learning Nondeterministic Classifiers. JJ Del Coz, J Díez, A Bahamonde Journal of Machine Learning Research 10 (10), 2009 | 110 | 2009 |
| Quantification-oriented learning based on reliable classifiers J Barranquero, J Díez, JJ del Coz Pattern Recognition 48 (2), 591-604, 2015 | 86 | 2015 |
| Automatic plankton quantification using deep features P González, A Castaño, EE Peacock, J Díez, JJ Del Coz, HM Sosik Journal of Plankton Research 41 (4), 449-463, 2019 | 74 | 2019 |
| Feature subset selection for learning preferences: A case study A Bahamonde, GF Bayón, J Díez, JR Quevedo, O Luaces, JJ Del Coz, ... Proceedings of the twenty-first international conference on Machine learning, 7, 2004 | 71 | 2004 |
| On the problem of error propagation in classifier chains for multi-label classification R Senge, JJ Del Coz, E Hüllermeier Data Analysis, Machine Learning and Knowledge Discovery, 163-170, 2014 | 69 | 2014 |
| On the study of nearest neighbor algorithms for prevalence estimation in binary problems J Barranquero, P González, J Díez, JJ Del Coz Pattern Recognition 46 (2), 472-482, 2013 | 67 | 2013 |
| Validation methods for plankton image classification systems P González, E Álvarez, J Díez, Á López‐Urrutia, JJ del Coz Limnology and Oceanography: Methods 15 (3), 221-237, 2017 | 66 | 2017 |
| Using ensembles for problems with characterizable changes in data distribution: A case study on quantification P Pérez-Gállego, JR Quevedo, JJ del Coz Information Fusion 34, 87-100, 2017 | 66 | 2017 |
| Deep learning to frame objects for visual target tracking S Pang, JJ del Coz, Z Yu, O Luaces, J Díez Engineering Applications of Artificial Intelligence 65, 406-420, 2017 | 64 | 2017 |
| Dynamic ensemble selection for quantification tasks P Pérez-Gállego, A Castaño, JR Quevedo, JJ del Coz Information Fusion 45, 1-15, 2019 | 63 | 2019 |
| Estimation of the exposure of buildings to driving rain in Spain from daily wind and rain data JM Pérez-Bella, J Domínguez-Hernández, B Rodríguez-Soria, ... Building and Environment 57, 259-270, 2012 | 60 | 2012 |
| Development of a distributive control scheme for fluorescent lighting based on LonWorks technology JM Alonso, J Ribas, JJD Coz, AJ Calleja, EL Corominas, M Rico-Secades IEEE Transactions on Industrial Electronics 47 (6), 1253-1262, 2000 | 58 | 2000 |
| Using artificial intelligence to design and implement a morphological assessment system in beef cattle F Goyache, JJ del Coz, JR Quevedo, S López, J Alonso, J Ranilla, ... Animal Science 73 (1), 49-60, 2001 | 52 | 2001 |
| Rectifying classifier chains for multi-label classification R Senge, JJ del Coz, E Hüllermeier arXiv preprint arXiv:1906.02915, 2019 | 50 | 2019 |
| How to learn consumer preferences from the analysis of sensory data by means of support vector machines (SVM) A Bahamonde, J Díez, JR Quevedo, O Luaces, JJ del Coz Trends in food science & technology 18 (1), 20-28, 2007 | 49 | 2007 |
| Why is quantification an interesting learning problem? P González, J Díez, N Chawla, JJ del Coz Progress in Artificial Intelligence 6, 53-58, 2017 | 48 | 2017 |
Jorge DíezORCID:0000-0002-1314-2441 , Artificial Intelligence Center, University of Oviedo at GijónDirección de correo verificada de uniovi.es
