Automatic Design of Decision-Tree Induction Algorithms (Paperback, 2015 ed.)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Computer Science
Release date:	February 2015
First published:	2015
Authors:	Rodrigo C Barros • Andre C.P.L.F. de Carvalho • Alex A. Freitas
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Paperback
Pages:	176
Edition:	2015 ed.
ISBN-13:	978-3-319-14230-2
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Databases > Data mining Promotions
LSN:	3-319-14230-5
Barcode:	9783319142302

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