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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions/ by Fawaz Alsolami, Mohammad Azad, Igor Chikalov, Mikhail Moshkov.
作者:	Alsolami, Fawaz.
其他作者:	Moshkov, Mikhail.
面頁冊數:	XVII, 276 p. 44 illus., 8 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Optimization. -
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
Alsolami, Fawaz.

Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions[electronic resource] /by Fawaz Alsolami, Mohammad Azad, Igor Chikalov, Mikhail Moshkov. - 1st ed. 2020. - XVII, 276 p. 44 illus., 8 illus. in color.online resource. - Intelligent Systems Reference Library,1561868-4394 ;. - Intelligent Systems Reference Library,67.

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:

669174
Optimization.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions
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