F. Herrera

Publications580
h-index97
Citations36,281
Highly Influential Citations1,949
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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
TLDR
We propose a taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based. Expand
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A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
TLDR
a b s t r a c t The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. Expand
  • 2,436
  • 114
  • PDF
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
TLDR
Training classifiers with datasets which suffer of imbalanced class distributions is an important problem in data mining. Expand
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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
TLDR
We present a case study which involves a set of techniques in classification tasks and we study the use of nonparametric statistical inference for analyzing the results obtained in an experiment design. Expand
  • 1,279
  • 57
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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
We focus our study on the use of statistical techniques in the analysis of evolutionary algorithms’ behaviour over optimization problems by using non-parametric test procedures. Expand
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An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons
In a recently published paper in JMLR, Demˇ sar (2006) recommends a set of non-parametric statistical tests and procedures which can be safely used for comparing the performance of classifiers over… Expand
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  • 47
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KEEL: a software tool to assess evolutionary algorithms for data mining problems
TLDR
This paper introduces a software tool named KEEL which is asoftware tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. Expand
  • 1,056
  • 47
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Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
TLDR
The nearest neighbor classifier suffers from several drawbacks such as high storage requirements, low efficiency in classification response, and low noise tolerance. Expand
  • 541
  • 46
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A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning
TLDR
The inductive learning of fuzzy rule-based classification systems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables becomes high. Expand
  • 274
  • 44
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Genetic fuzzy systems: taxonomy, current research trends and prospects
  • F. Herrera
  • Computer Science
  • Evol. Intell.
  • 10 January 2008
TLDR
The use of genetic algorithms for designing fuzzy systems provides them with the learning and adaptation capabilities and is called genetic fuzzy systems (GFSs). Expand
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