A memetic algorithm for evolutionary prototype selection: A scaling up approach

TL;DR: A comprehensive multi-facet survey of recent research in memetic computation is presented and includes simple hybrids, adaptive hybrids and memetic automaton.

TL;DR: This work is focused on presenting a survey of the main instance selection methods reported in the literature, and shows how the training set is reduced which allows reducing runtimes in the classification and/or training stages of classifiers.

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.

TL;DR: A thorough experimental analysis in a series of big datasets is carried out that provides guidelines as to how to use the k‐nearest neighbor algorithm to obtain Smart/Quality Data for a high‐quality data mining process.

TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.

TL;DR: This handbook provides you with everything you need to know about parametric and nonparametric statistical procedures, and helps you choose the best test for your data, interpret the results, and better evaluate the research of others.

TL;DR: In the second edition, the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations as discussed by the authors.