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Albert Orriols-Puig
Researcher at La Salle University
Publications - 48
Citations - 1325
Albert Orriols-Puig is an academic researcher from La Salle University. The author has contributed to research in topics: Learning classifier system & Supervised learning. The author has an hindex of 17, co-authored 48 publications receiving 1192 citations. Previous affiliations of Albert Orriols-Puig include University of Illinois at Urbana–Champaign & Ramon Llull University.
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A study of the effect of different types of noise on the precision of supervised learning techniques
TL;DR: Naïve Bayes appears as the most robust algorithm, and SMO the least, relative to the other two techniques, however, the underlying empirical behavior of the techniques is more complex, and varies depending on the noise type and the specific data set being processed.
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Evolutionary rule-based systems for imbalanced data sets
TL;DR: This paper adapts and analyzes LCSs for challenging imbalanced data sets and establishes the bases for further studying the combination of re-sampling technique and learner best suited to a specific kind of problem.
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Genetic-based machine learning systems are competitive for pattern recognition
TL;DR: The state of the art in GBML is reviewed, some of the best representatives of different families are selected, and the accuracy and the interpretability of their models are compared, which can be used as recommendation guidelines on which systems should be employed depending on whether the user prefers to maximize the accuracy or theinterpretability of the models.
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Fuzzy-UCS: A Michigan-Style Learning Fuzzy-Classifier System for Supervised Learning
TL;DR: Fuzzy-UCS is inspired by UCS, an on-line accuracy-based learning classifier system that introduces a linguistic representation of the rules with the aim of evolving more readable rule sets, while maintaining similar performance and generalization capabilities to those presented by UCS.
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Facetwise Analysis of XCS for Problems With Class Imbalances
TL;DR: XCS is identified as the sweet spot where XCS is able to scalably and efficiently evolve accurate models of rare classes, and facetwise analysis is used as a tool for designing a set of configuration guidelines that have to be followed to ensure convergence.