Applicability of Estimation of Distribution Algorithms to the Fuzzy Rule Learning Problem: A Preliminary Study. - M. Julia Flores; José A. Gámez 001 (ICEIS, 2002)

Plug & Play Object Oriented Bayesian Networks. - Olav Bangsø; M. Julia Flores; Finn Verner Jensen (CAEPIA, 2003)

Incremental compilation of Bayesian networks. - M. Julia Flores; José A. Gámez 001; Kristian G. Olesen (UAI, 2003)

Triangulation of Bayesian networks by retriangulation. - M. Julia Flores; José A. Gámez 001 (Int. J. Intell. Syst., 2003)

Abductive Inference in Bayesian Networks: Finding a Partition of the Explanation Space. - M. Julia Flores; José A. Gámez 001; Serafín Moral (ECSQARU, 2005)

Breeding Value Classification in Manchego Sheep: A Study of Attribute Selection and Construction. - M. Julia Flores; José A. Gámez 001 (KES (2), 2005)

Learning Linguistic Fuzzy Rules by Using Estimation of Distribution Algorithms as the Search Engine in the COR Methodology. - M. Julia Flores; José A. Gámez 001; José M. Puerta (Towards a New Evolutionary Computation, 2006)

The Independency tree model: a new approach for clustering and factorisation. - M. Julia Flores; José A. Gámez 001; Serafín Moral (Probabilistic Graphical Models, 2006)

Seleccion genetica para la mejora de la raza ovina manchega mediante tecnicas de Mineria de Datos. - M. Julia Flores; José A. Gámez 001; Juan L. Mateo; José M. Puerta (Inteligencia Artif., 2006)

Initial breeding value prediction on Manchego sheep by using rule-based systems. - Luis de la Ossa; M. Julia Flores; José A. Gámez 001; Juan L. Mateo; José M. Puerta (Expert Syst. Appl., 2007)

Use of Explanation Treesto Describe the State Space of a Probabilistic-Based Abduction Problem. - M. Julia Flores; José A. Gámez 001; Serafín Moral (Innovations in Bayesian Networks, 2008)

The PDG-Mixture Model for Clustering. - M. Julia Flores; José A. Gámez 001; Jens Dalgaard Nielsen (DaWaK, 2009)

HODE: Hidden One-Dependence Estimator. - M. Julia Flores; José A. Gámez 001; Ana M. Martínez; José M. Puerta (ECSQARU, 2009)

GAODE and HAODE: two proposals based on AODE to deal with continuous variables. - M. Julia Flores; José A. Gámez 001; Ana M. Martínez; José M. Puerta (ICML, 2009)

Analyzing the Impact of the Discretization Method When Comparing Bayesian Classifiers. - M. Julia Flores; José A. Gámez 001; Ana M. Martínez; José M. Puerta (IEA/AIE (1), 2010)

Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers. - M. Julia Flores; José A. Gámez 001; Ana M. Martínez; Antonio Salmerón (ISDA, 2011)

Handling numeric attributes when comparing Bayesian network classifiers: does the discretization method matter? - M. Julia Flores; José A. Gámez 001; Ana M. Martínez; José M. Puerta (Appl. Intell., 2011)

Incorporating expert knowledge when learning Bayesian network structure: A medical case study. - M. Julia Flores; Ann E. Nicholson; Andrew Brunskill; Kevin B. Korb; Steven Mascaro (Artif. Intell. Medicine, 2011)

Incremental Compilation of Bayesian Networks Based on Maximal Prime Subgraphs. - M. Julia Flores; José A. Gámez 001; Kristian G. Olesen (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2011)

Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers. - Ana M. Martínez; Geoffrey I. Webb; M. Julia Flores; José A. Gámez 001 (HAIS (2), 2012)

Evaluation of a Thermal-Comfort Control System Using Real Data. - Pablo Bermejo 001; Luis Redondo; Luis delaOssa; Daniel Rodríguez; M. Julia Flores; Carmen Urea; José A. Gámez 001; Jesus Martínez-Gómez; José M. Puerta (KES, 2012)

Domains of competence of the semi-naive Bayesian network classifiers. - M. Julia Flores; José A. Gámez 001; Ana M. Martínez (Inf. Sci., 2014)

Impact on Bayesian Networks Classifiers When Learning from Imbalanced Datasets. - M. Julia Flores; José A. Gámez 001 (ICAART (2), 2015)

Comparison between Bayesian network classifiers and SVMs for semantic localization. - Fernando Rubio 002; Jesus Martínez-Gómez; M. Julia Flores; José M. Puerta (Expert Syst. Appl., 2016)

Drawing a baseline in aesthetic quality assessment. - Fernando Rubio 002; M. Julia Flores; José M. Puerta (ICMV, 2017)

Integration of contextual information into the scene classification problem. - Fernando Rubio 002; Jesus Martínez-Gómez; M. Julia Flores; José M. Puerta (Robotics Auton. Syst., 2017)

Machine learning for music genre: multifaceted review and experimentation with audioset. - Jaime Ramírez 001; M. Julia Flores (J. Intell. Inf. Syst., 2020)

Data Capture and Multimodal Learning Analytics Focused on Engagement With a New Wearable IoT Approach. - Vicente López Camacho; Elena de la Guía; Teresa Olivares; M. Julia Flores; Luis Orozco-Barbosa (IEEE Trans. Learn. Technol., 2020)

Web-Based Music Genre Classification for Timeline Song Visualization and Analysis. - Jaime Ramírez Castillo; M. Julia Flores (IEEE Access, 2021)

An Application for Aesthetic Quality Assessment in Photography with Interpretability Features. - Fernando Rubio Perona; María Julia Flores Gallego; José Miguel Puerta Callejón (Entropy, 2021)

Ranking-based scores for the assessment of aesthetic quality in photography. - Fernando Rubio 002; M. Julia Flores; José M. Puerta (Signal Process. Image Commun., 2022)

Novel groundtruth transformations for the aesthetic assessment problem. - Luis Gonzalez-Naharro; M. Julia Flores; Jesus Martínez-Gómez; José M. Puerta (Inf. Process. Manag., 2023)

Applying ranking techniques for estimating influence of Earth variables on temperature forecast error. - M. Julia Flores; Melissa Ruiz-Vásquez; Ana Bastos; Rene Orth (CoRR, 2024)

Predicting spotify audio features from Last.fm tags. - Jaime Ramírez Castillo; M. Julia Flores; Philippe Leray 001 (Multim. Tools Appl., 2024)

Evaluation of data augmentation techniques on subjective tasks. - Luis Gonzalez-Naharro; M. Julia Flores; Jesus Martínez-Gómez; José M. Puerta (Mach. Vis. Appl., 2024)

User-centric Music Recommendations. - Jaime Ramírez Castillo; M. Julia Flores; Ann E. Nicholson (CoRR, 2025)