Table of contents
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.
- DOI https://doi.org/10.1007/978-3-319-14231-9
- Copyright Information The Author(s) 2015
- Publisher Name Springer, Cham
- eBook Packages Computer Science
- Print ISBN 978-3-319-14230-2
- Online ISBN 978-3-319-14231-9
- Series Print ISSN 2191-5768
- Series Online ISSN 2191-5776
- About this book