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Andres R. Masegosa
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Year
Requirements for total uncertainty measures in Dempster–Shafer theory of evidence
J Abellán, A Masegosa
International journal of general systems 37 (6), 733-747, 2008
962008
A method for integrating expert knowledge when learning Bayesian networks from data
A Cano, AR Masegosa, S Moral
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41 …, 2011
832011
An interactive approach for Bayesian network learning using domain/expert knowledge
AR Masegosa, S Moral
International Journal of Approximate Reasoning 54 (8), 1168-1181, 2013
792013
Learning under Model Misspecification: Applications to Variational and Ensemble methods
A Masegosa
Advances in Neural Information Processing Systems 33, 2020
742020
Bagging schemes on the presence of class noise in classification
J Abellán, AR Masegosa
Expert Systems with Applications 39 (8), 6827-6837, 2012
712012
Bagging decision trees on data sets with classification noise
J Abellán, AR Masegosa
Foundations of Information and Knowledge Systems: 6th International …, 2010
672010
An ensemble method using credal decision trees
J Abellan, AR Masegosa
European journal of operational research 205 (1), 218-226, 2010
582010
Second order PAC-Bayesian bounds for the weighted majority vote
A Masegosa, S Lorenzen, C Igel, Y Seldin
Advances in Neural Information Processing Systems 33, 2020
382020
Classification with decision trees from a nonparametric predictive inference perspective
J Abellán, RM Baker, F Coolen, RJ Crossman, AR Masegosa
Computational Statistics & Data Analysis 71, 789-802, 2014
352014
Learning from incomplete data in Bayesian networks with qualitative influences
AR Masegosa, AJ Feelders, LC van der Gaag
International Journal of Approximate Reasoning 69, 18-34, 2016
332016
An experimental study about simple decision trees for bagging ensemble on datasets with classification noise
J Abellán, AR Masegosa
Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 10th …, 2009
332009
Modeling concept drift: A probabilistic graphical model based approach
H Borchani, AM Martínez, AR Masegosa, H Langseth, TD Nielsen, ...
International Symposium on Intelligent Data Analysis, 72-83, 2015
302015
New skeleton-based approaches for Bayesian structure learning of Bayesian networks
AR Masegosa, S Moral
Applied Soft Computing 13 (2), 1110-1120, 2013
292013
Diversity and Generalization in Neural Network Ensembles
LA Ortega, R Cabañas, A Masegosa
International Conference on Artificial Intelligence and Statistics, 11720-11743, 2022
282022
Imprecise classification with credal decision trees
J Abellan, AR Masegosa
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2012
272012
Bayesian models of data streams with hierarchical power priors
A Masegosa, TD Nielsen, H Langseth, D Ramos-López, A Salmerón, ...
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
252017
AMIDST: A Java toolbox for scalable probabilistic machine learning
AR Masegosa, AM Martínez, D Ramos-López, R Cabañas, A Salmerón, ...
Knowledge-Based Systems 163, 595-597, 2019
222019
Probabilistic Graphical Models on Multi-Core CPUs Using Java 8
AR Masegosa, AM Martinez, H Borchani
IEEE Computational Intelligence Magazine 11 (2), 41-54, 2016
182016
Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks
AR Masegosa, S Moral
International Journal of Approximate Reasoning 55 (7), 1548-1569, 2014
182014
From anecdote to evidence: the relationship between personality and need for cognition of developers
D Russo, AR Masegosa, KJ Stol
Empirical Software Engineering 27 (3), 1-29, 2022
172022
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Articles 1–20