Aritz Pérez Martínez

Orcid: 0000-0002-8128-1099

According to our database1, Aritz Pérez Martínez authored at least 68 papers between 2006 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Fast $K$-Medoids With the $l_{1}$-Norm.
IEEE Trans. Artif. Intell., April, 2024

A probabilistic generative model to discover the treatments of coexisting diseases with missing data.
Comput. Methods Programs Biomed., January, 2024

Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences.
Stat. Comput., 2024

Risk-based Calibration for Probabilistic Classifiers.
CoRR, 2024

PAC-Bayes-Chernoff bounds for unbounded losses.
CoRR, 2024

2023
Comparing Two Samples Through Stochastic Dominance: A Graphical Approach.
J. Comput. Graph. Stat., April, 2023

On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels.
Knowl. Inf. Syst., January, 2023

Learning the progression patterns of treatments using a probabilistic generative model.
J. Biomed. Informatics, January, 2023

LASSO for streaming data with adaptative filtering.
Stat. Comput., 2023

Fast computation of cluster validity measures for bregman divergences and benefits.
Pattern Recognit. Lett., 2023

Time-dependent Probabilistic Generative Models for Disease Progression.
CoRR, 2023

Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems.
CoRR, 2023

Structural Restricted Boltzmann Machine for image denoising and classification.
CoRR, 2023

On the Fair Comparison of Optimization Algorithms in Different Machines.
CoRR, 2023

Efficient Learning of Minimax Risk Classifiers in High Dimensions.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

2022
An Efficient Split-Merge Re-Start for the $K$K-Means Algorithm.
IEEE Trans. Knowl. Data Eng., 2022

Generalized Maximum Entropy for Supervised Classification.
IEEE Trans. Inf. Theory, 2022

Non-parametric discretization for probabilistic labeled data.
Pattern Recognit. Lett., 2022

An active adaptation strategy for streaming time series classification based on elastic similarity measures.
Neural Comput. Appl., 2022

Are the statistical tests the best way to deal with the biomarker selection problem?
Knowl. Inf. Syst., 2022

On the relative value of weak information of supervision for learning generative models: An empirical study.
Int. J. Approx. Reason., 2022

Machine Learning From Crowds Using Candidate Set-Based Labeling.
IEEE Intell. Syst., 2022

Semantic Clustering of a Sequence of Satellite Images.
CoRR, 2022

Dirichlet process mixture models for non-stationary data streams.
Proceedings of the IEEE International Conference on Data Mining, 2022

Implementing the cumulative difference plot in the IOHanalyzer.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

2021
A Cheap Feature Selection Approach for the K-Means Algorithm.
IEEE Trans. Neural Networks Learn. Syst., 2021

Statistical model for reproducibility in ranking-based feature selection.
Knowl. Inf. Syst., 2021

MRCpy: A Library for Minimax Risk Classifiers.
CoRR, 2021

Rank Aggregation for Non-stationary Data Streams.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

K-means for Evolving Data Streams.
Proceedings of the IEEE International Conference on Data Mining, 2021

2020
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem.
Swarm Evol. Comput., 2020

Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences.
Neural Networks, 2020

An efficient K-means clustering algorithm for tall data.
Data Min. Knowl. Discov., 2020

Passive Approach for the K-means Problem on Streaming Data.
CoRR, 2020

Learning decomposable models by coarsening.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Minimax Classification with 0-1 Loss and Performance Guarantees.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

An adaptive neuroevolution-based hyperheuristic.
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

General Supervision via Probabilistic Transformations.
Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

2019
Supervised non-parametric discretization based on Kernel density estimation.
Pattern Recognit. Lett., 2019

On-line Elastic Similarity Measures for time series.
Pattern Recognit., 2019

Online Ranking with Concept Drifts in Streaming Data.
CoRR, 2019

Supervised classification via minimax probabilistic transformations.
CoRR, 2019

On the evaluation and selection of classifier learning algorithms with crowdsourced data.
Appl. Soft Comput., 2019

Approaching the quadratic assignment problem with kernels of mallows models under the hamming distance.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

2018
Let nature decide its nature: On the design of collaborative hyperheuristics for decentralized ephemeral environments.
Future Gener. Comput. Syst., 2018

Weak Labeling for Crowd Learning.
CoRR, 2018

An efficient K -means clustering algorithm for massive data.
CoRR, 2018

Approximating the maximum weighted decomposable graph problem with applications to probabilistic graphical models.
Proceedings of the International Conference on Probabilistic Graphical Models, 2018

Adversarial Sample Crafting for Time Series Classification with Elastic Similarity Measures.
Proceedings of the Intelligent Distributed Computing XII, 2018

Are the Artificially Generated Instances Uniform in Terms of Difficulty?
Proceedings of the 2018 IEEE Congress on Evolutionary Computation, 2018

Crowd Learning with Candidate Labeling: An EM-Based Solution.
Proceedings of the Advances in Artificial Intelligence, 2018

2017
An efficient approximation to the K-means clustering for massive data.
Knowl. Based Syst., 2017

On-Line Dynamic Time Warping for Streaming Time Series.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Nature-inspired approaches for distance metric learning in multivariate time series classification.
Proceedings of the 2017 IEEE Congress on Evolutionary Computation, 2017

2016
Efficient approximation of probability distributions with k-order decomposable models.
Int. J. Approx. Reason., 2016

An efficient K-means algorithm for Massive Data.
CoRR, 2016

2015
Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species.
Ecol. Informatics, 2015

2014
Learning Maximum Weighted (k+1)-Order Decomposable Graphs by Integer Linear Programming.
Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014

2013
A general framework for the statistical analysis of the sources of variance for classification error estimators.
Pattern Recognit., 2013

Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting.
Environ. Model. Softw., 2013

Multidimensional k-Interaction Classifier: Taking Advantage of All the Information Contained in Low Order Interactions.
Proceedings of the Advances in Artificial Intelligence, 2013

2012
Using Multidimensional Bayesian Network Classifiers to Assist the Treatment of Multiple Sclerosis.
IEEE Trans. Syst. Man Cybern. Part C, 2012

2010
Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation.
IEEE Trans. Pattern Anal. Mach. Intell., 2010

2009
Bayesian classifiers based on kernel density estimation: Flexible classifiers.
Int. J. Approx. Reason., 2009

2008
How Trustworthy is Crafty's Analysis of Chess Champions?
J. Int. Comput. Games Assoc., 2008

2006
Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes.
Int. J. Approx. Reason., 2006

Machine learning in bioinformatics.
Briefings Bioinform., 2006

Information Theory and Classification Error in Probabilistic Classifiers.
Proceedings of the Discovery Science, 9th International Conference, 2006


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