Metalearning (2^nd Ed., 2nd ed. 2022) Applications to Automated Machine Learning and Data Mining Cognitive Technologies Series

This open access book offers a comprehensive and thorough introduction to almost all aspects of metalearning and automated machine learning (AutoML), covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience.

As one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, AutoML is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user.

This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence.

Pavel B. Brazdil is a senior researcher at LIAAD INESC TEC, Porto and Full Professor at FEP, University of Porto, Portugal and since 2019, Professor Emeritus. He obtained his PhD in machine learning in 1981 at the University of Edinburgh. Since the 1990s he has pioneered the area of metalearning and supervised various PhD students in this area. His main interests lie in machine learning, data mining, algorithm selection, metalearning, AutoML and text mining, among others. He has edited 6 books and more than 110 papers referenced on Google Scholar, of which approximately 80 are also on ISI/DBLP/Scopus. He was a program chair of various machine learning conferences (e.g., in 1992,2005), has co-organized various workshops on metalearning and acted as a co-editor of two special issues of MLJ on this topic. He is a member of the editorial board of the Machine Learning Journal and a Fellow of EurAI.

Jan N. van Rijn obtained his PhD in Computer Sciencein 2016 at Leiden Institute of Advanced Computer Science (LIACS), Leiden University (the Netherlands). During his PhD, he made several funded research visits to the University of Waikato (New Zealand) and University of Porto (Portugal). After obtaining his PhD, he worked as a postdoctoral researcher in the Machine Learning lab at University of Freiburg (Germany), headed by Prof. Dr. Frank Hutter, after which he moved to work as a postdoctoral researcher at Columbia University in the City of New York (USA). He currently holds a position as assistant professor at LIACS, Leiden University. His research aim is to democratize the access to machine learning and artificial intelligence across societal institutions. He is one of the founders of OpenML.org, an open science platform for machine learning. His research interests include artificial intelligence, automated machine learning and metalearning.

Carlos Soares is an Associate Professor at the Faculty of Engineering of U. Porto.