Information Theory and Classification Error in Probabilistic Classifiers. - Aritz Pérez Martínez; Pedro Larrañaga; Iñaki Inza (Discovery Science, 2006)

Machine learning in bioinformatics. - Pedro Larrañaga; Borja Calvo; Roberto Santana 001; Concha Bielza; Josu Galdiano; Iñaki Inza; José Antonio Lozano 001; Rubén Armañanzas; Guzmán Santafé; Aritz Pérez Martínez; Victor Robles (Briefings Bioinform., 2006)

Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes. - Aritz Pérez Martínez; Pedro Larrañaga; Iñaki Inza (Int. J. Approx. Reason., 2006)

How Trustworthy is Crafty's Analysis of Chess Champions? - Matej Guid; Aritz Pérez; Ivan Bratko (J. Int. Comput. Games Assoc., 2008)

Bayesian classifiers based on kernel density estimation: Flexible classifiers. - Aritz Pérez Martínez; Pedro Larrañaga; Iñaki Inza (Int. J. Approx. Reason., 2009)

Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation. - Juan Diego Rodríguez; Aritz Pérez Martínez; José Antonio Lozano 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

Using Multidimensional Bayesian Network Classifiers to Assist the Treatment of Multiple Sclerosis. - Juan Diego Rodríguez; Aritz Pérez Martínez; David Arteta; Diego Tejedor; José Antonio Lozano 001 (IEEE Trans. Syst. Man Cybern. Part C, 2012)

Multidimensional k-Interaction Classifier: Taking Advantage of All the Information Contained in Low Order Interactions. - Aritz Pérez Martínez; José Antonio Lozano 001; Iñaki Inza (CAEPIA, 2013)

Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting. - Jose A. Fernandes; José Antonio Lozano 001; Iñaki Inza; Xabier Irigoien; Aritz Pérez; Juan Diego Rodríguez (Environ. Model. Softw., 2013)

A general framework for the statistical analysis of the sources of variance for classification error estimators. - Juan Diego Rodríguez; Aritz Pérez Martínez; José Antonio Lozano 001 (Pattern Recognit., 2013)

Learning Maximum Weighted (k+1)-Order Decomposable Graphs by Integer Linear Programming. - Aritz Pérez; Christian Blum 001; José Antonio Lozano 001 (Probabilistic Graphical Models, 2014)

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species. - Jose A. Fernandes; Xabier Irigoien; José Antonio Lozano 001; Iñaki Inza; Nerea Goikoetxea; Aritz Pérez Martínez (Ecol. Informatics, 2015)

An efficient K-means algorithm for Massive Data. - Marco Capó; Aritz Pérez; José Antonio Lozano 001 (CoRR, 2016)

Efficient approximation of probability distributions with k-order decomposable models. - Aritz Pérez; Iñaki Inza; José Antonio Lozano 001 (Int. J. Approx. Reason., 2016)

Nature-inspired approaches for distance metric learning in multivariate time series classification. - Izaskun Oregi; Javier Del Ser; Aritz Pérez; José Antonio Lozano 001 (CEC, 2017)

On-Line Dynamic Time Warping for Streaming Time Series. - Izaskun Oregi; Aritz Pérez; Javier Del Ser; José Antonio Lozano 001 (ECML/PKDD (2), 2017)

An efficient approximation to the K-means clustering for massive data. - Marco Capó; Aritz Pérez; José Antonio Lozano 001 (Knowl. Based Syst., 2017)

Crowd Learning with Candidate Labeling: An EM-Based Solution. - Iker Beñaran-Muñoz; Jerónimo Hernández-González; Aritz Pérez (CAEPIA, 2018)

Are the Artificially Generated Instances Uniform in Terms of Difficulty? - Aritz Pérez; Josu Ceberio; José Antonio Lozano 001 (CEC, 2018)

Adversarial Sample Crafting for Time Series Classification with Elastic Similarity Measures. - Izaskun Oregi; Javier Del Ser; Aritz Pérez; José Antonio Lozano 001 (IDC, 2018)

Approximating the maximum weighted decomposable graph problem with applications to probabilistic graphical models. - Aritz Pérez; Christian Blum 001; José Antonio Lozano 001 (PGM, 2018)

An efficient K -means clustering algorithm for massive data. - Marco Capó; Aritz Pérez; José Antonio Lozano 001 (CoRR, 2018)

Weak Labeling for Crowd Learning. - Iker Beñaran-Muñoz; Jerónimo Hernández-González; Aritz Pérez (CoRR, 2018)

Let nature decide its nature: On the design of collaborative hyperheuristics for decentralized ephemeral environments. - Aritz Pérez Martínez; Eneko Osaba; Miren Nekane Bilbao; Javier Del Ser (Future Gener. Comput. Syst., 2018)

Approaching the quadratic assignment problem with kernels of mallows models under the hamming distance. - Etor Arza; Josu Ceberio; Aritz Pérez; Ekhine Irurozki (GECCO (Companion), 2019)

On the evaluation and selection of classifier learning algorithms with crowdsourced data. - Ari Urkullu; Aritz Pérez; Borja Calvo (Appl. Soft Comput., 2019)

Supervised classification via minimax probabilistic transformations. - Santiago Mazuelas; Andrea Zanoni; Aritz Pérez (CoRR, 2019)

Online Ranking with Concept Drifts in Streaming Data. - Ekhine Irurozki; Jesus L. Lobo; Aritz Pérez; Javier Del Ser (CoRR, 2019)

On-line Elastic Similarity Measures for time series. - Izaskun Oregi; Aritz Pérez; Javier Del Ser; José Antonio Lozano 001 (Pattern Recognit., 2019)

Supervised non-parametric discretization based on Kernel density estimation. - Jose Luis Flores 001; Borja Calvo; Aritz Pérez (Pattern Recognit. Lett., 2019)

General Supervision via Probabilistic Transformations. - Santiago Mazuelas; Aritz Pérez (ECAI, 2020)

An adaptive neuroevolution-based hyperheuristic. - Etor Arza; Josu Ceberio; Aritz Pérez; Ekhiñe Irurozki (GECCO Companion, 2020)

Minimax Classification with 0-1 Loss and Performance Guarantees. - Santiago Mazuelas; Andrea Zanoni; Aritz Pérez (NeurIPS, 2020)

Learning decomposable models by coarsening. - George Orfanides; Aritz Pérez (PGM, 2020)

Passive Approach for the K-means Problem on Streaming Data. - Arkaitz Bidaurrazaga; Aritz Pérez; Marco Capó (CoRR, 2020)

An efficient K-means clustering algorithm for tall data. - Marco Capó; Aritz Pérez; José Antonio Lozano 001 (Data Min. Knowl. Discov., 2020)

Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences. - Izaskun Oregi; Javier Del Ser; Aritz Pérez; José Antonio Lozano 001 (Neural Networks, 2020)

Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem. - Etor Arza; Aritz Pérez; Ekhiñe Irurozki; Josu Ceberio (Swarm Evol. Comput., 2020)

K-means for Evolving Data Streams. - Arkaitz Bidaurrazaga; Aritz Pérez; Marco Capó (ICDM, 2021)

Rank Aggregation for Non-stationary Data Streams. - Ekhine Irurozki; Aritz Pérez; Jesus L. Lobo; Javier Del Ser (ECML/PKDD (3), 2021)

MRCpy: A Library for Minimax Risk Classifiers. - Kartheek Bondugula; Santiago Mazuelas; Aritz Pérez (CoRR, 2021)

Statistical model for reproducibility in ranking-based feature selection. - Ari Urkullu; Aritz Pérez; Borja Calvo (Knowl. Inf. Syst., 2021)

A Cheap Feature Selection Approach for the K-Means Algorithm. - Marco Capó; Aritz Pérez; José Antonio Lozano 001 (IEEE Trans. Neural Networks Learn. Syst., 2021)

Implementing the cumulative difference plot in the IOHanalyzer. - Etor Arza; Josu Ceberio; Ekhiñe Irurozki; Aritz Pérez (GECCO Companion, 2022)

Dirichlet process mixture models for non-stationary data streams. - Ioar Casado; Aritz Pérez (ICDM, 2022)

Semantic Clustering of a Sequence of Satellite Images. - Carlos Echegoyen; Aritz Pérez; Guzmán Santafé; Unai Pérez-Goya; María Dolores Ugarte (CoRR, 2022)

Machine Learning From Crowds Using Candidate Set-Based Labeling. - Iker Beñaran-Muñoz; Jerónimo Hernández-González; Aritz Pérez (IEEE Intell. Syst., 2022)

On the relative value of weak information of supervision for learning generative models: An empirical study. - Jerónimo Hernández-González; Aritz Pérez (Int. J. Approx. Reason., 2022)

Are the statistical tests the best way to deal with the biomarker selection problem? - Ari Urkullu; Aritz Pérez; Borja Calvo (Knowl. Inf. Syst., 2022)

An active adaptation strategy for streaming time series classification based on elastic similarity measures. - Izaskun Oregi; Aritz Pérez; Javier Del Ser; José Antonio Lozano 001 (Neural Comput. Appl., 2022)

Non-parametric discretization for probabilistic labeled data. - Jose Luis Flores 001; Borja Calvo; Aritz Pérez (Pattern Recognit. Lett., 2022)

Generalized Maximum Entropy for Supervised Classification. - Santiago Mazuelas; Yuan Shen 001; Aritz Pérez (IEEE Trans. Inf. Theory, 2022)

An Efficient Split-Merge Re-Start for the $K$K-Means Algorithm. - Marco Capó; Aritz Pérez; José Antonio Lozano 001 (IEEE Trans. Knowl. Data Eng., 2022)

Efficient Learning of Minimax Risk Classifiers in High Dimensions. - Kartheek Bondugula; Santiago Mazuelas; Aritz Pérez (UAI, 2023)

On the Fair Comparison of Optimization Algorithms in Different Machines. - Etor Arza; Josu Ceberio; Ekhiñe Irurozki; Aritz Pérez (CoRR, 2023)

Structural Restricted Boltzmann Machine for image denoising and classification. - Arkaitz Bidaurrazaga; Aritz Pérez; Roberto Santana 001 (CoRR, 2023)

Time-dependent Probabilistic Generative Models for Disease Progression. - Onintze Zaballa; Aritz Pérez; Elisa Gómez-Inhiesto; Teresa Acaiturri Ayesta; José Antonio Lozano 001 (CoRR, 2023)

Fast computation of cluster validity measures for bregman divergences and benefits. - Marco Capó; Aritz Pérez; José Antonio Lozano 001 (Pattern Recognit. Lett., 2023)

LASSO for streaming data with adaptative filtering. - Marco Capó; Aritz Pérez; José Antonio Lozano 001 (Stat. Comput., 2023)

Learning the progression patterns of treatments using a probabilistic generative model. - Onintze Zaballa; Aritz Pérez; Elisa Gómez-Inhiesto; Teresa Acaiturri Ayesta; José Antonio Lozano 001 (J. Biomed. Informatics, 2023)

On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels. - Iker Beñaran-Muñoz; Jerónimo Hernández-González; Aritz Pérez (Knowl. Inf. Syst., 2023)

Comparing Two Samples Through Stochastic Dominance: A Graphical Approach. - Etor Arza; Josu Ceberio; Ekhiñe Irurozki; Aritz Pérez (J. Comput. Graph. Stat., 2023)

PAC-Bayes-Chernoff bounds for unbounded losses. - Ioar Casado; Luis A. Ortega Andrés; Aritz Pérez; Andrés R. Masegosa (NeurIPS, 2024)

Risk-based Calibration for Probabilistic Classifiers. - Aritz Pérez; Carlos Echegoyen; Guzmán Santafé (CoRR, 2024)

Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences. - Carlos Echegoyen; Aritz Pérez; Guzmán Santafé; Unai Pérez-Goya; María Dolores Ugarte (Stat. Comput., 2024)

A probabilistic generative model to discover the treatments of coexisting diseases with missing data. - Onintze Zaballa; Aritz Pérez; Elisa Gómez-Inhiesto; Teresa Acaiturri Ayesta; José Antonio Lozano 001 (Comput. Methods Programs Biomed., 2024)

Fast $K$-Medoids With the $l_{1}$-Norm. - Marco Capó; Aritz Pérez; José Antonio Lozano 001 (IEEE Trans. Artif. Intell., 2024)

Speeding-Up Evolutionary Algorithms to Solve Black-Box Optimization Problems. - Judith Echevarrieta; Etor Arza; Aritz Pérez (IEEE Trans. Evol. Comput., 2025)

Decentralized Federated Learning of Probabilistic Generative Classifiers. - Aritz Pérez; Carlos Echegoyen; Guzmán Santafé (CoRR, 2025)