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

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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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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions is written by Fawaz Alsolami; Mohammad Azad; Igor Chikalov; Mikhail Moshkov and published by Springer. The Digital and eTextbook ISBNs for Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions are 9783030128548, 3030128547 and the print ISBNs are 9783030128531, 3030128539.