S. García

Publications223
h-index54
Citations23,111
Highly Influential Citations1,067
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A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
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KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework
TLDR
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.
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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
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An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
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Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
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Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
TLDR
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.
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KEEL: a software tool to assess evolutionary algorithms for data mining problems
This paper introduces a software tool named KEEL which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification,…
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A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization
TLDR
This study analyzes the published results for the algorithms presented in the CEC’2005 Special Session on Real Parameter Optimization by using non-parametric test procedures and states that a parametric statistical analysis could not be appropriate specially when the authors deal with multiple-problem results.
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An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons
TLDR
The paper correctly introduces the basic procedures and some of the most advanced ones when comparing a control method, but it does not deal with some advanced topics in depth.
A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning
TLDR
A survey of discretization methods proposed in the literature from a theoretical and empirical perspective and a taxonomy based on the main properties pointed out in previous research is developed, including all the known methods up to date.
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