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Open access
Title
Metalearning : applications to automated machine learning and data mining / Pavel Brazdil, Jan N. van Rijn, Carlos Soares, Joaquin Vanschoren.
Edition
Second edition.
ISBN
9783030670245 (electronic bk.)
3030670244 (electronic bk.)
9783030670238 (print)
Published
Cham, Switzerland : Springer, 2022.
Language
English
Description
1 online resource (xii, 346 pages) : illustrations (some color).
Item Number
10.1007/978-3-030-67024-5 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
This open access book 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, automated machine learning (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 offers a comprehensive and thorough introduction to almost all aspects of metalearning and 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. 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.
Note
Includes index.
Access Note
Open access.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 2, 2022).
Series
Cognitive technologies, 2197-6635
Introduction
Part I, Basic Architecture of Metalearning and AutoML Systems
Metalearning Approaches for Algorithm Selection I
Evaluating Recommendations of Metalearning / AutoML Systems
Metalearning Approaches for Algorithm Selection II
Automating Machine Learning (AutoML) and Algorithm Configuration
Dataset Characteristics (Metafeatures)
Automating the Workflow / Pipeline Design
Part II, Extending the Architecture of Metalearning and AutoML Systems
Setting Up Configuration Spaces and Experiments
Using Metalearning in the Construction of Ensembles
Algorithm Recommendation for Data Streams
Transfer of Metamodels Across Tasks
Automating Data Science
Automating the Design of Complex Systems
Repositories of Experimental Results (OpenML)
Learning from Metadata in Repositories.
