Decision and inhibitory trees and rules for decision tables with many valued dec
Uiterlijk 4 december in huis
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 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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Oorspronkelijke releasedatum: 27 november 2020
Aantal pagina's: 276
Hoofdauteur: Fawaz Alsolami
Tweede Auteur: Mohammad Azad
Co Auteur: Igor Chikalov
Hoofduitgeverij: Springer Nature Switzerland AG
Editie: 1st ed. 2020
Product breedte: 155 mm
Product lengte: 235 mm
Verpakking breedte: 155 mm
Verpakking hoogte: 235 mm
Verpakking lengte: 235 mm
Verpakkingsgewicht: 456 g
EAN: 9783030128562