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Automatic Design of Decision-Tree Induction Algorithms

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Automatic Design of Decision-Tree Induction Algorithms/ by Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas.
Author:	Barros, Rodrigo C.
other author:	de Carvalho, André C.P.L.F.
Description:	XII, 176 p. 18 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-319-14231-9
ISBN:	9783319142319

Automatic Design of Decision-Tree Induction Algorithms
Barros, Rodrigo C.

Automatic Design of Decision-Tree Induction Algorithms[electronic resource] /by Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas. - 1st ed. 2015. - XII, 176 p. 18 illus.online resource. - 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

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

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Automatic Design of Decision-Tree Induction Algorithms
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