Summary Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the
process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that …

Learning with imbalanced data is one of the recent challenges in machine learning. Various
solutions have been proposed in order to find a treatment for this problem, such as …

Rapid developments in the field of genetic algorithms along with the popularity of the first
edition precipitated this completely revised, thoroughly updated second edition of The …

Gene expression data usually contain a large number of genes, but a small number of
samples. Feature selection for gene expression data aims at finding a set of genes that best …

Feature selection is a common and key problem in many classification and regression tasks.
It can be viewed as a multiobjective optimisation problem, since, in the simplest case, it …

OBJECTIVE: Accurate classification methods are critical in computer-aided diagnosis
(CADx) and other clinical decision support systems. Previous research has reported on …

Feature and instance selection are two effective data reduction processes which can be
applied to classification tasks obtaining promising results. Although both processes are …

Abstract Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the
process of Darwinian evolution. The motivation for applying EAs to Data Mining is that they …

A hybrid evolutionary learning algorithm is presented that synthesizes a complete multiclass
pattern recognition system. The approach uses a multifaceted representation that evolves …

General dissimilarity-based learning approaches have been proposed for dissimilarity data
sets [1, 2]. They often arise in problems in which direct comparisons of objects are made by …