J
Joaquín Derrac
Researcher at Cardiff University
Publications - 38
Citations - 8076
Joaquín Derrac is an academic researcher from Cardiff University. The author has contributed to research in topics: k-nearest neighbors algorithm & Evolutionary algorithm. The author has an hindex of 21, co-authored 38 publications receiving 6573 citations. Previous affiliations of Joaquín Derrac include University of Granada.
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Journal ArticleDOI
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
TL;DR: The basics are discussed and a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis are given.
Proceedings Article
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework
Jesús Alcalá-Fdez,Alberto Fernández,Julián Luengo,Joaquín Derrac,Salvador García,Luciano Sánchez,Francisco Herrera +6 more
TL;DR: The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformat and some guidelines for including new algorithms in KEEL, helping the researcher to compare the results of many approaches already included within the KEEL software.
Journal ArticleDOI
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
TL;DR: A taxonomy based on the main characteristics presented in prototype selection is proposed and an experimental study involving different sizes of data sets is conducted for measuring their performance in terms of accuracy, reduction capabilities, and runtime.
Journal ArticleDOI
A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification
TL;DR: This paper provides a survey of PG methods specifically designed for the NN rule, and proposes a taxonomy based on the main characteristics presented in them that is appropriate for application to different datasets.
Journal ArticleDOI
Evolutionary-based selection of generalized instances for imbalanced classification
TL;DR: This paper proposes a method belonging to the family of the nested generalized exemplar that accomplishes learning by storing objects in Euclidean n-space that outperforms other classic and recent models in accuracy and requires to store a lower number of generalized examples.