Andrés R. Masegosa

发表

A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data

Andrés Cano, Andrés R. Masegosa, Serafín Moral, 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Financial Data Analysis with PGMs Using AMIDST

Andrés R. Masegosa, Anders L. Madsen, Rafael Cabañas, 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

Imprecise Classification with Credal Decision Trees

Andrés R. Masegosa, Joaquín Abellán, A. Masegosa, 2012, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Scaling up Bayesian variational inference using distributed computing clusters

Andrés R. Masegosa, Anders L. Madsen, Antonio Salmerón, 2017, Int. J. Approx. Reason..

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach

Andrés R. Masegosa, Anders L. Madsen, Ana M. Martínez, 2016, Probabilistic Graphical Models.

Bayesian Models of Data Streams with Hierarchical Power Priors

Andrés R. Masegosa, Anders L. Madsen, Darío Ramos-López, 2017, ICML.

Split Criterions for Variable Selection Using Decision Trees

Andrés R. Masegosa, Joaquín Abellán, 2007, ECSQARU.

Variational Inference over Nonstationary Data Streams for Exponential Family Models

Andrés R. Masegosa, Antonio Salmerón, Darío Ramos-López, 2020, Mathematics.

An Experimental Study about Simple Decision Trees for Bagging Ensemble on Datasets with Classification Noise

Andrés R. Masegosa, Joaquín Abellán, A. Masegosa, 2009, ECSQARU.

Learning from incomplete data in Bayesian networks with qualitative influences

Linda C. van der Gaag, A. J. Feelders, Andrés R. Masegosa, 2016, Int. J. Approx. Reason..

New skeleton-based approaches for Bayesian structure learning of Bayesian networks

Andrés R. Masegosa, Serafín Moral, S. Moral, 2013, Appl. Soft Comput..

Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models

Andrés R. Masegosa, Rafael Cabañas, Andrés Cano, 2018, IPMU.

InferPy: Probabilistic modeling with Tensorflow made easy

Andrés R. Masegosa, Antonio Salmerón, Rafael Cabañas, 2019, Knowl. Based Syst..

Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks

Andrés R. Masegosa, Rafael Rumí, Anders L. Madsen, 2018, Int. J. Approx. Reason..

Rejoinder on "Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks"

Andrés R. Masegosa, Serafín Moral, 2014, Int. J. Approx. Reason..

Selective Gaussian Naïve Bayes Model for Diffuse Large-B-Cell Lymphoma Classification: Some Improvements in Preprocessing and Variable Elimination

Andrés R. Masegosa, Serafín Moral, Francisco Javier García Castellano, 2005, ECSQARU.

Evaluating Query-Independent Object Features for Relevancy Prediction

Joemon M. Jose, Hideo Joho, Andrés R. Masegosa, 2007, ECIR.

Probabilistic Graphical Models on Multi-Core CPUs Using Java 8

Andrés R. Masegosa, Ana M. Martínez, Hanen Borchani, 2016, IEEE Computational Intelligence Magazine.

A Semi-naive Bayes Classifier with Grouping of Cases

Andrés R. Masegosa, Serafín Moral, Joaquín Abellán, 2007, ECSQARU.

Modeling Concept Drift: A Probabilistic Graphical Model Based Approach

Andrés R. Masegosa, Anders L. Madsen, Antonio Salmerón, 2015, IDA.

Requirement Engineering for a Small Project with Pre-Specified Scope

Andrés R. Masegosa, Sigve Hovda, Anders L. Madsen, 2014, NIK.

Locally Averaged Bayesian Dirichlet Metrics

Andrés R. Masegosa, Serafín Moral, Andrés Cano, 2011, ECSQARU.

A Java Toolbox for Analysis of MassIve Data STreams using Probabilistic Graphical Models

Andrés R. Masegosa, Anders L. Madsen, Antonio Salmerón, 2016 .

Bagging schemes on the presence of class noise in classification

Andrés R. Masegosa, Joaquín Abellán, A. Masegosa, 2012, Expert Syst. Appl..

d-VMP: Distributed Variational Message Passing

Andrés R. Masegosa, Anders L. Madsen, Ana M. Martínez, 2016, Probabilistic Graphical Models.

A Bayesian Random Split to Build Ensembles of Classification Trees

Andrés R. Masegosa, Serafín Moral, Andrés Cano, 2009, ECSQARU.

AMIDST: Analysis of MassIve Data STreams

Andrés R. Masegosa, Anders L. Madsen, Antonio Salmerón, 2015 .

InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy

Andrés R. Masegosa, Javier Cózar, Antonio Salmerón, 2020, Neurocomputing.

Methods to Determine the Branching Attribute in Bayesian Multinets Classifiers

Andrés R. Masegosa, Serafín Moral, Francisco Javier García Castellano, 2005, ECSQARU.

Varying Parameter in Classification Based on Imprecise Probabilities

Andrés R. Masegosa, Serafín Moral, Joaquín Abellán, 2006, SMPS.

Dynamic Bayesian modeling for risk prediction in credit operations

Andrés R. Masegosa, Anders L. Madsen, Antonio Salmerón, 2015, SCAI.

Probabilistic Models with Deep Neural Networks

Andrés R. Masegosa, Antonio Salmerón, Rafael Cabañas, 2019, Entropy.

Requirements for total uncertainty measures in Dempster–Shafer theory of evidence

Andrés R. Masegosa, Joaquín Abellán, A. Masegosa, 2008, Int. J. Gen. Syst..

A Filter-Wrapper Method to Select Variables for the Naive Bayes Classifier Based on Credal Decision Trees

Andrés R. Masegosa, Joaquín Abellán, 2009, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks

Andrés R. Masegosa, Serafín Moral, Andrés Cano, 2013, Int. J. Approx. Reason..

An interactive approach for cleaning noisy observations in Bayesian networks with the help of an expert

Andrés R. Masegosa, 2012 .

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs

Andrés R. Masegosa, Anders L. Madsen, Ana M. Martínez, 2016, ECAI.

MAP inference in dynamic hybrid Bayesian networks

Andrés R. Masegosa, Anders L. Madsen, Ana M. Martínez, 2017, Progress in Artificial Intelligence.

A memory efficient semi-Naive Bayes classifier with grouping of cases

Andrés R. Masegosa, Serafín Moral, Joaquín Abellán, 2011, Intell. Data Anal..

Bagging Decision Trees on Data Sets with Classification Noise

Andrés R. Masegosa, Joaquín Abellán, A. Masegosa, 2010, FoIKS.

Stochastic Discriminative EM pdf

Andrés R. Masegosa, A. Masegosa, 2014, UAI.

Haplotype-based Classifiers to Predict Individual Susceptibility to Complex Diseases - An Example for Multiple Sclerosis

Andrés R. Masegosa, María M. Abad-Grau, Serafín Moral, 2012, BIOINFORMATICS.

Combining Decision Trees Based on Imprecise Probabilities and Uncertainty Measures

Andrés R. Masegosa, Joaquín Abellán, A. Masegosa, 2007, ECSQARU.

Link-Based Text Classification Using Bayesian Networks

Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, 2009, INEX.

International Journal of Approximate Reasoning

Andrés R. Masegosa, Serafín Moral, S. Moral, 2022 .

Classification with decision trees from a nonparametric predictive inference perspective

Frank P. A. Coolen, Andrés R. Masegosa, Joaquín Abellán, 2014, Comput. Stat. Data Anal..

Parallel Importance Sampling in Conditional Linear Gaussian Networks

Andrés R. Masegosa, Anders L. Madsen, Ana M. Martínez, 2015, CAEPIA.

Effects of highly agreed documents in relevancy prediction

Joemon M. Jose, Hideo Joho, Andrés R. Masegosa, 2007, SIGIR.

An ensemble method using credal decision trees

Andrés R. Masegosa, Joaquín Abellán, A. Masegosa, 2010, Eur. J. Oper. Res..

Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks

Andrés R. Masegosa, Serafín Moral, S. Moral, 2014, Int. J. Approx. Reason..

Riskoweb: Web-Based Genetic Profiling to Complex Disease Using Genome-Wide SNP Markers

Andrés R. Masegosa, María M. Abad-Grau, Rosana Montes-Soldado, 2011, PACBB.

Analyzing concept drift: A case study in the financial sector

Andrés R. Masegosa, Antonio Salmerón, Ana M. Martínez, 2020, Intell. Data Anal..

An Importance Sampling Approach to Integrate Expert Knowledge When Learning Bayesian Networks From Data

Andrés R. Masegosa, Serafín Moral, Andrés Cano, 2010, IPMU.

Variational Robust Subspace Clustering with Mean Update Algorithm

Shinichi Nakajima, Sergej Dogadov, Andrés R. Masegosa, 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

A Bayesian stochastic search method for discovering Markov boundaries

Andrés R. Masegosa, Serafín Moral, S. Moral, 2012, Knowl. Based Syst..