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Automatic design of decision-tree Induction algorithms[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	005.1
書名/作者:	Automatic design of decision-tree Induction algorithms/ by Rodrigo C. Barros, Andre C.P.L.F de Carvalho, Alex A. Freitas.
作者:	Barros, Rodrigo C.
其他作者:	Carvalho, Andre C.P.L.F. de.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xii, 176 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Computer algorithms.
標題:	Decision trees.
標題:	Computer Science.
標題:	Data Mining and Knowledge Discovery.
標題:	Pattern Recognition.
ISBN:	9783319142319 (electronic bk.)
ISBN:	9783319142302 (paper)
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-14231-9

Automatic design of decision-tree Induction algorithms[electronic resource] /
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:

179921
Computer algorithms.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 005.1

Automatic design of decision-tree Induction algorithms[electronic resource] /
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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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