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Studies in Computational Intelligence

Meta-Learning in Decision Tree Induction

Authors: Grąbczewski, Krzysztof

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  • Presents a general meta-learning approach which is applicable to a variety of machine learning algorithms
  • Focuses on different variants of decision tree induction
  • Details the long and complex road from various small and larger algorithms to a unified approach and the robustness of meta-learning
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eBook 117,69 €
price for Spain (gross)
  • ISBN 978-3-319-00960-5
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
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  • Immediate eBook download after purchase
Hardcover 166,39 €
price for Spain (gross)
  • ISBN 978-3-319-00959-9
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Softcover 145,78 €
price for Spain (gross)
  • ISBN 978-3-319-37723-0
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

The book focuses on different variants of decision tree induction but also describes  the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.

 

Table of contents (7 chapters)

Table of contents (7 chapters)

Buy this book

eBook 117,69 €
price for Spain (gross)
  • ISBN 978-3-319-00960-5
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 166,39 €
price for Spain (gross)
  • ISBN 978-3-319-00959-9
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 145,78 €
price for Spain (gross)
  • ISBN 978-3-319-37723-0
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Meta-Learning in Decision Tree Induction
Authors
Series Title
Studies in Computational Intelligence
Series Volume
498
Copyright
2014
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-00960-5
DOI
10.1007/978-3-319-00960-5
Hardcover ISBN
978-3-319-00959-9
Softcover ISBN
978-3-319-37723-0
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XVI, 343
Number of Illustrations
33 b/w illustrations
Topics