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Automatic design of decision-tree In...

Barros, Rodrigo C.

Automatic design of decision-tree Induction algorithms

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automatic design of decision-tree Induction algorithmsby Rodrigo C. Barros, Andre C.P.L.F de Carvalho, Alex A. Freitas.
Author:	Barros, Rodrigo C.
other author:	Carvalho, Andre C.P.L.F. de.
Published:	Cham :Springer International Publishing :2015.
Description:	xii, 176 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Computer algorithms.
Online resource:	http://dx.doi.org/10.1007/978-3-319-14231-9
ISBN:	9783319142319 (electronic bk.)

Automatic design of decision-tree Induction algorithms
Barros, Rodrigo C.

Automatic design of decision-tree Induction algorithms[electronic resource] /by Rodrigo C. Barros, Andre C.P.L.F de Carvalho, Alex A. Freitas. - Cham :Springer International Publishing :2015. - xii, 176 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Introduction -- Decision-Tree Induction -- Evolutionary Algorithms and Hyper-Heuristics -- HEAD-DT: Automatic Design of Decision-Tree Algorithms -- HEAD-DT: Experimental Analysis -- HEAD-DT: Fitness Function Analysis -- Conclusions.

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.

ISBN: 9783319142319 (electronic bk.)

Standard No.: 10.1007/978-3-319-14231-9doiSubjects--Topical Terms:

184478
Computer algorithms.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 005.1

Automatic design of decision-tree Induction algorithms
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245 1 0 $a Automatic design of decision-tree Induction algorithms $h [electronic resource] / $c by Rodrigo C. Barros, Andre C.P.L.F de Carvalho, Alex A. Freitas.
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490 1 $a SpringerBriefs in computer science, $x 2191-5768
505 0 $a Introduction -- Decision-Tree Induction -- Evolutionary Algorithms and Hyper-Heuristics -- HEAD-DT: Automatic Design of Decision-Tree Algorithms -- HEAD-DT: Experimental Analysis -- HEAD-DT: Fitness Function Analysis -- Conclusions.
520 $a 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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