The vast majority of the literature evaluates the performance of classification models using
only the criterion of predictive accuracy. This paper reviews the case for considering also the …

Evolutionary algorithm (EA) is an umbrella term used to describe population‐based
stochastic direct search algorithms that in some sense mimic natural evolution. Prominent …

In the last decade, with availability of large datasets and more computing power, machine
learning systems have achieved (super) human performance in a wide variety of tasks …

This paper presents a benchmark of supervised Automated Machine Learning (AutoML)
tools. Firstly, we analyze the characteristics of eight recent open-source AutoML tools (Auto …

Scoring systems are linear classification models that only require users to add, subtract and
multiply a few small numbers in order to make a prediction. These models are in widespread …

This paper presents a survey of evolutionary algorithms that are designed for decision-tree
induction. In this context, most of the paper focuses on approaches that evolve decision …

Objective Manual evaluation of machine learning algorithms and selection of a suitable
classifier from the list of available candidate classifiers, is highly time consuming and …

Data clustering is a popular unsupervised data mining tool that is used for partitioning a
given dataset into homogeneous groups based on some similarity/dissimilarity metric …

This paper reviews the application of multiobjective optimization in the fields of
bioinformatics and computational biology. A survey of existing work, organized by …

A key component for the performance, availability, and reliability of power grids is the power
transformer. Although power transformers are very reliable assets, the early detection of …