Overview
- Provides a detailed and up-to-date view on the top-down induction of decision trees
- Introduces a novel hyper-heuristic approach that is capable of automatically designing top-down decision-tree induction algorithms
- Discusses two frameworks in which the hyper-heuristic can be executed in order to generate tailor-made decision-tree induction algorithms
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (7 chapters)
Keywords
About this book
Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics.
"Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.
Authors and Affiliations
Bibliographic Information
Book Title: Automatic Design of Decision-Tree Induction Algorithms
Authors: Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-14231-9
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2015
Softcover ISBN: 978-3-319-14230-2Published: 03 March 2015
eBook ISBN: 978-3-319-14231-9Published: 04 February 2015
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XII, 176
Number of Illustrations: 18 b/w illustrations
Topics: Data Mining and Knowledge Discovery, Pattern Recognition