Téléchargez le livre : Automatic Design of Decision-Tree Induction Algorithms
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Description

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.
Pages
176 pages
Collection
SpringerBriefs in Computer Science
Parution
2015-02-04
Marque
Springer
EAN papier
9783319142302
EAN EPUB
9783319142319
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
17
Taille du fichier
2263 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
58,01 €

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