| Dependent binary relevance models for multi-label classification E Montañes, R Senge, J Barranquero, JR Quevedo, JJ del Coz, ... Pattern Recognition 47 (3), 1494-1508, 2014 | 153 | 2014 |
| Multilabel classifiers with a probabilistic thresholding strategy JR Quevedo, O Luaces, A Bahamonde Pattern Recognition 45 (2), 876-883, 2012 | 88 | 2012 |
| 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 | 85 | 2001 |
| 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 | 68 | 2004 |
| 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 | 57 | 2017 |
| Graphical feature selection for multilabel classification tasks G Lastra, O Luaces, JR Quevedo, A Bahamonde Advances in Intelligent Data Analysis X: 10th International Symposium, IDA …, 2011 | 47 | 2011 |
| 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 | 46 | 2001 |
| Dynamic ensemble selection for quantification tasks P Pérez-Gállego, A Castano, JR Quevedo, JJ del Coz Information Fusion 45, 1-15, 2019 | 44 | 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 | 44 | 2007 |
| Artificial intelligence techniques point out differences in classification performance between light and standard bovine carcasses J Dıez, A Bahamonde, J Alonso, S López, JJ Del Coz, JR Quevedo, ... Meat Science 64 (3), 249-258, 2003 | 40 | 2003 |
| Analyzing sensory data using non-linear preference learning with feature subset selection O Luaces, GF Bayón, JR Quevedo, J Díez, JJ Del Coz, A Bahamonde Machine Learning: ECML 2004: 15th European Conference on Machine Learning …, 2004 | 36 | 2004 |
| Genetical genomics: use all data M Pérez-Enciso, JR Quevedo, A Bahamonde BMC genomics 8 (1), 1-8, 2007 | 35 | 2007 |
| Aggregating independent and dependent models to learn multi-label classifiers E Montanés, JR Quevedo, JJ del Coz Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 34 | 2011 |
| An overview of inference methods in probabilistic classifier chains for multilabel classification D Mena, E Montañés, JR Quevedo, JJ del Coz Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6 (6 …, 2016 | 25 | 2016 |
| Discovering relevancies in very difficult regression problems: applications to sensory data analysis J Díez Peláez, G Fernández Bayón, JR Quevedo Pérez, JJ Coz Velasco, ... Proceedings of the European conference on artificial intelligence (ECAI’04), 2004 | 25 | 2004 |
| Using A* for inference in probabilistic classifier chains D Mena Waldo, E Montañés Roces, JR Quevedo Pérez, JJ Coz Velasco Proceedings of the Twenty-Fourth International Joint Conference on …, 2015 | 24 | 2015 |
| A wrapper approach with support vector machines for text categorization E Montañés, JR Quevedo, I Díaz Computational Methods in Neural Modeling: 7th International Work-Conference …, 2003 | 23 | 2003 |
| Viability of an alarm predictor for coffee rust disease using interval regression O Luaces, LHA Rodrigues, CA Alves Meira, JR Quevedo, A Bahamonde Trends in Applied Intelligent Systems: 23rd International Conference on …, 2010 | 20 | 2010 |
| Collaborative tag recommendation system based on logistic regression E Montanés, JR Quevedo, I Díaz, J Ranilla ECML PKDD Discovery Challenge, 173-188, 2009 | 19 | 2009 |
| A simple and efficient method for variable ranking according to their usefulness for learning JR Quevedo, A Bahamonde, O Luaces Computational statistics & data analysis 52 (1), 578-595, 2007 | 19 | 2007 |
Juan José del CozArtificial Intelligent Center - University of OviedoGeverifieerd e-mailadres voor uniovi.es
