For a given specific machine learning task, very often several machine learning algorithms
and their right configurations are tested in a trial-and-error approach, until an adequate …

The recent evolution of machine learning (ML) algorithms and the high level of expertise
required to use them have fuelled the demand for non-experts solutions. The selection of an …

The selection of the optimal ensembles of classifiers in multiple-classifier selection
technique is un-decidable in many cases and it is potentially subjected to a trial-and-error …

In this paper, we propose meta-leaming as a general technique to combine the results of
multiple learning algorithms, each applied to a set of training data. We detail several …

There are many knowledge-based data mining frameworks and it is common to think that
new ones cannot come up with anything new. This article refutes such claims. We propose a …

Abstract Framework for Similarity-Based Methods (SBMs) allows to create many algorithms
that differ in important aspects. Although no single learning algorithm may outperform other …

Meta-learning, as applied to model selection, consists of inducing mappings from tasks to
learners. Traditionally, tasks are characterised by the values of pre-computed meta …

The algorithm selection problem [Rice 1976] seeks to answer the question: Which algorithm
is likely to perform best for my problem? Recognizing the problem as a learning task in the …

Many studies in machine learning try to investigate what makes an algorithm succeed or fail
on certain datasets. However, the field is still evolving relatively quickly, and new algorithms …

The performance of most of the classification algorithms on a particular dataset is highly
dependent on the learning parameters used for training them. Different approaches like grid …