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Automatic Design of Decision-Tree Induction Algorithms

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.

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Detail Information
Series Title
-
Call Number
006.312
Publisher
Cham : .,
Collation
XII, 176
Language
English
ISBN/ISSN
978-3-319-14231-9
Classification
006.312
Content Type
-
Media Type
computer
Carrier Type
online resource
Edition
-
Subject(s)
Data mining
Specific Detail Info
-
Statement of Responsibility
Other Information
Cataloger
ratna
Source
https://link.springer.com/book/10.1007/978-3-319-14231-9
Validator
-
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