Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

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 …

Automated machine learning (AutoML) seeks to automatically select, compose, and
parametrize machine learning algorithms, so as to achieve optimal performance on a given …

Workflow technology is rapidly evolving and, rather than being limited to modeling the
control flow in business processes, is becoming a key mechanism to perform advanced data …

Algorithm selection (AS), selecting the algorithm best suited for a particular problem
instance, is acknowledged to be a key issue to make the best out of algorithm portfolios. This …

Background Various kinds of data mining algorithms are continuously raised with the
development of related disciplines. The applicable scopes and their performances of these …

Abstract Knowledge extraction and incorporation is currently considered to be beneficial for
efficient Big Data analytics. Knowledge can take part in workflow design, constraint …

Over the last decade, the long-running endeavour to automate high-level processes in
machine learning (ML) has risen to mainstream prominence, stimulated by advances in …

This paper introduces an Automated Machine Learning (AutoML) framework specifically
designed to efficiently synthesize end-to-end multimodal machine learning pipelines …

Recent meta-learning approaches are oriented towards algorithm selection, optimization or
recommendation of existing algorithms. In this article we show how data-tailored algorithms …