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Decision and inhibitory trees and ru...

Alsolami, Fawaz.

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Decision and inhibitory trees and rules for decision tables with many-valued decisions

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Decision and inhibitory trees and rules for decision tables with many-valued decisions/ by Fawaz Alsolami ... [et al.].
其他作者:	Alsolami, Fawaz.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xvii, 276 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Decision trees. -
電子資源:	https://doi.org/10.1007/978-3-030-12854-8
ISBN:	9783030128548

Decision and inhibitory trees and rules for decision tables with many-valued decisions
Decision and inhibitory trees and rules for decision tables with many-valued decisions[electronic resource] /by Fawaz Alsolami ... [et al.]. - Cham :Springer International Publishing :2020. - xvii, 276 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1561868-4394 ;. - Intelligent systems reference library ;v.156..

The results presented here (including the assessment of a new tool - inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions. The authors consider various examples of problems and corresponding decision tables with many-valued decisions, discuss the difference between decision and inhibitory trees and rules, and develop tools for their analysis and design. Applications include the study of totally optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory trees and rules; the comparison of greedy heuristics for tree and rule construction as single-criterion and bi-criteria optimization algorithms; and the development of a restricted multi-pruning approach used in classification and knowledge representation.

ISBN: 9783030128548

Standard No.: 10.1007/978-3-030-12854-8doiSubjects--Topical Terms:

827433
Decision trees.

LC Class. No.: HD30.23

Dewey Class. No.: 658.403

Decision and inhibitory trees and rules for decision tables with many-valued decisions
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