We investigate the learning of quantitative structure activity relationships (QSARs) as a case-
study of meta-learning. This application area is of the highest societal importance, as it is a …

One of the challenges in Machine Learning to find a classifier and parameter settings that
work well on a given dataset. Evaluating all possible combinations typically takes too much …

Algorithm selection methods can be speeded-up substantially by incorporating multi-
objective measures that give preference to algorithms that are both promising and fast to …

Machine learning, already at the core of increasingly many systems and applications, is set
to become even more ubiquitous with the rapid rise of wearable devices and the Internet of …

Defining the algorithm capable of best fit the characteristics observed for a problem is a
complex task in the context of combinatorial optimization problems. As a decision-making …

Safety managers, practitioners, and researchers can employ different models for estimating
and assessing hazards, consequences, likelihoods, risks, and/or mitigation measures in the …

Model building is a time-consuming task that has already been practised for a long time by
many scientists. In the 16th century, Nicolaus Copernicus described a model that considered …

The average ranking method (AR) is one of the simplest and effective algorithms selection
methods. This method uses metadata in the form of test results of a given set of algorithms …

Identifying the best machine learning algorithm for a given problem continues to be an active
area of research. In this paper we present a new method which exploits both meta-level …

Abstract Machine learning users need methods that can help them identify algorithms or
even workflows (combination of algorithms with preprocessing tasks, using or not …