Automatic Design of Decision-Tree Induction Algorithms

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Database Management, General Computing
Cover of the book Automatic Design of Decision-Tree Induction Algorithms by Rodrigo C. Barros, Alex A. Freitas, André C.P.L.F de Carvalho, Springer International Publishing
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Author: Rodrigo C. Barros, Alex A. Freitas, André C.P.L.F de Carvalho ISBN: 9783319142319
Publisher: Springer International Publishing Publication: February 4, 2015
Imprint: Springer Language: English
Author: Rodrigo C. Barros, Alex A. Freitas, André C.P.L.F de Carvalho
ISBN: 9783319142319
Publisher: Springer International Publishing
Publication: February 4, 2015
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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