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

Nonfiction, Science & Nature, Mathematics, Applied, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions by Fawaz Alsolami, Mohammad Azad, Igor Chikalov, Mikhail Moshkov, Springer International Publishing
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Author: Fawaz Alsolami, Mohammad Azad, Igor Chikalov, Mikhail Moshkov ISBN: 9783030128548
Publisher: Springer International Publishing Publication: March 13, 2019
Imprint: Springer Language: English
Author: Fawaz Alsolami, Mohammad Azad, Igor Chikalov, Mikhail Moshkov
ISBN: 9783030128548
Publisher: Springer International Publishing
Publication: March 13, 2019
Imprint: Springer
Language: English

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.

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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.

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