This tutorial will introduce and discuss state of the art methods in meta-learning, algorithm selection, and algorithm configuration, which are increasingly relevant today. Researchers and practitioners from all areas of science and technology face a large choice of parameterized machine learning algorithms, with little guidance as to when and how to use which technique. Data mining challenges frequently remind us that algorithm selection and configuration play a crucial role in achieving cutting-edge performance, and are indispensible in industrial applications.
Meta-learning leverages knowledge of past applications of algorithms to learn how to select the best techniques for future applications, and offers effective techniques that are superior to humans both in terms of the quality of the end result and even more so in the time required to achieve it. Recent approaches also include (preferably very fast) partial probing runs on a given problem with the aim of determining the best strategy to be used from there onwards. This may include further probing or recommending an algorithm to be used to solve the given problem. A recent trend is to incorporate such techniques into software platforms. This synergy leads to new advances that recommend combinations of algorithms and hyperparameter settings simultaneously, and that speed up algorithm configuration by learning which parameter settings are likely most useful for dealing with the data at hand.
After motivating and introducing the concepts of algorithm selection and configuration, we elucidate how they arise in machine learning and data mining, but also in other domains, such as optimization. We demonstrate how meta-learning techniques can be effectively used in this context, exploiting information gleaned from past experiments as well as by probing the data at hand. Moreover, many current applications require the use of machine learning or data mining workflows that consist of several different processes or operations. Constructing such complex systems or workflows requires extensive expertise, as well as existing meta-data and software, and can be greatly facilitated by leveraging the methodologies presented at this tutorial.
Please see the resources below for all the slides.