Meta-Learning in Decision Tree Induction

  • Krzysztof Grąbczewski

Part of the Studies in Computational Intelligence book series (SCI, volume 498)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Krzysztof Grąbczewski
    Pages 1-9
  3. Krzysztof Grąbczewski
    Pages 11-117
  4. Krzysztof Grąbczewski
    Pages 119-137
  5. Krzysztof Grąbczewski
    Pages 139-181
  6. Krzysztof Grąbczewski
    Pages 183-231
  7. Krzysztof Grąbczewski
    Pages 233-317
  8. Krzysztof Grąbczewski
    Pages 319-323
  9. Back Matter
    Pages 325-343

About this book

Introduction

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.

 

Keywords

Computational Intelligence Machine Learning Decision Tree Induction Meta-Learning

Authors and affiliations

  • Krzysztof Grąbczewski
    • 1
  1. 1.Department of Informatics, Faculty of Physics, Astronomy and InformaticsNicolaus Copernicus UniversityToruńPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-00960-5
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-00959-9
  • Online ISBN 978-3-319-00960-5
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book