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+34 946 567 842
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+34 946 567 842
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aperez@bcamath.org
Information of interest
- Orcid: 0000-0002-8128-1099
Postdoc Fellow at BCAM. The main methodological research lines include probabilistic graphical models, supervised classification, information theory, density estimation and feature subset selection. The methodological contributions have been applied to the fields of bioinformatics (genetics and epigenetics) and ecological modelling (fisheries).
BayesianTree
Approximating probability distributions with mixtures of decomposable models
Authors: Aritz Pérez
License: free and open source software
Placement
Local
KmeansLandscape
Study the k-means problem from a local optimization perspective
Authors: Aritz Pérez
License: free and open source software
Placement
Local
PGM
Procedures for learning probabilistic graphical models
Authors: Aritz Pérez
License: free and open source software
Placement
Local
On-line Elastic Similarity Measures
Adaptation of the most frequantly used elastic similarity measures: Dynamic Time Warping (DTW), Edit Distance (Edit), Edit Distance for Real Sequences (EDR) and Edit Distance with Real Penalty (ERP) to on-line setting.
Authors: Izaskun Oregi, Aritz Perez, Javier Del Ser, Jose A. Lozano
License: free and open source software
MRCpy: a library for Minimax Risk Classifiers
MRCpy library implements minimax risk classifiers (MRCs) that are based on robust risk minimization and can utilize 0-1-loss.
Authors: Kartheek Reddy, Claudia Guerrero, Aritz Perez, Santiago Mazuelas
License: free and open source software
OPTECOT - Optimal Evaluation Cost Tracking
This repository contains supplementary material for the paper Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems. In this work, we have presented OPTECOT (Optimal Evaluation Cost Tracking): a technique to reduce the cost of solving a computationally expensive black-box optimization problem using population-based algorithms, avoiding loss of solution quality. OPTECOT requires a set of approximate objective functions of different costs and accuracies, obtained by modifying a strategic parameter in the definition of the original function. The proposal allows the selection of the lowest cost approximation with the trade-off between cost and accuracy in real time during the algorithm execution. To solve an optimization problem different from those addressed in the paper, the repository also contains a library to apply OPTECOT with the CMA-ES (Covariance Matrix Adaptation Evolution Strategy) optimization algorithm.
Authors: Judith Echevarrieta, Etor Arza, Aritz Pérez
License: free and open source software
TransfHH
A multi-domain methodology to analyze an optimization problem set
Authors: Etor Arza, Ekhiñe Irurozki, Josu Ceberio, Aritz Perez
License: free and open source software
FractalTree
Implementation of the procedures presented in A. Pérez, I. Inza and J.A. Lozano (2016). Efficient approximation of probability distributions with k-order decomposable models. International Journal of Approximate Reasoning 74, 58-87.
Authors: Aritz Pérez
License: free and open source software
MixtureDecModels
Learning mixture of decomposable models with hidden variables
Authors: Aritz Pérez
License: free and open source software
Placement
Local