A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

By Salvador Garcia, Alberto Fernández, Julián Luengo, Francisco Herrera. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2009

697 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/79217fc643a2ee73b2d36c12f97dbd3b8d85be45

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In Artificial Intelligence

Citations in the corpus

KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework (2011, methodology): “Figure 6 also shows another type of results flow, the node Stat-Clas-Friedman which represents the statistical comparison, results are collected and a statistical analysis over multiple data sets is performed by following the indications given in [18].”
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics (2012, methodology): “We consider the use of non-parametric tests, according to the recommendations made in Demšar (2006), García and Herrera (2008), García et al. (2009), García, Fernández, Luengo, and Herrera (2010) where a set of simple, safe and robust non-parametric tests for statistical comparisons of classifiers…”
Monotonic Calibrated Interpolated Look-Up Tables (2015, background): “Similarly, Garcia et al. (2009) measure the interpretability of rule-based classiﬁers in terms of the number of rules and number of features used.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “[349] are popular in which the authors have selected 14 UCI datasets to compare the performance of five evolutionary algorithm used for classification purpose.”
SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory (2012, methodology): “In this paper, we use the hypothesis testing techniques to provide statistical support to the analysis of the results [18,34].”
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power (2010, background): “However, some studies involving CI algorithms in experimental comparisons show that these conditions are not easy to meet [21] [23] [47].”
Differential Evolution With Ranking-Based Mutation Operators (2013, methodology): “In addition, the multip leproblem statistical analysis based on the Wilcoxon’s test, a similar done in [35], [36], between ranking-based DE and non ranking-based DE is reported for all functions in Tables V and VI, respectively.”
Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019, methodology): “Statistical test for performance comparison In order to provide statistical support to the results obtained in the experimentation, a hypothesis testing technique is used [82].”
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches (2012, methodology): “To do so, we use nonparametric tests according to the recommendations made in [63]–[65], [108], where a set of proper nonparametric tests for statistical comparisons of classifiers is presented.”
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes (2011, methodology): “We consider the use of non-parametric tests according to the recommendations made in [20,35,33,34], where a set of simple, safe and robust non-parametric tests for statistical comparisons of classifiers is presented.”
A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm (2012, background): “Their advantage over ANOVA is that they do not require normality or homoscedasticity, assumptions that are often violated in machine learning studies [73, 74].”
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms (2011, methodology): “Instatisticalterms,weare losing the control on the Family-Wise Error Rate (FWER), defined as the probability of making one or more false discoveries among all the hypotheses when performing multiple pairwise tests.”
Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review (2020, methodology): “[29] Non-parametric - Classification Study of test application prerequisite in a classification context and with different measures.”