Classification is the key and most widely studied paradigm in machine learning community.
The selection of appropriate classification algorithm for a particular problem is a challenging …

Research and industry increasingly make use of large amounts of data to guide decision-
making. To do this, however, data needs to be analyzed in typically nontrivial refinement …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural
networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging …

Current benchmark reports of classification algorithms generally concern common classifiers
and their variants but do not include many algorithms that have been introduced in recent …

Many sciences have made significant breakthroughs by adopting online tools that help
organize, structure and mine information that is too detailed to be printed in journals. In this …

The success of hand-crafted machine learning systems in many applications raises the
question of making machine learning algorithms more autonomous, ie, to reduce the …

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a
per-instance basis in order to exploit the varying performance of algorithms over a set of …

The evaluation of artificial intelligence systems and components is crucial for the progress of
the discipline. In this paper we describe and critically assess the different ways AI systems …

Meta-learning is increasingly used to support the recommendation of machine learning
algorithms and their configurations. These recommendations are made based on meta-data …