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

Fawaz Alsolami

ISBN 10: 3030128539 / ISBN 13: 9783030128531

Verlag: Springer Nature Switzerland AG, Cham, 2019

Beschreibung

Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Bestandsnummer des Verkäufers 9783030128531

Bibliografische Details

• Titel: Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions (Hardcover)
• Verlag: Springer Nature Switzerland AG, Cham
• Erscheinungsjahr: 2019
• Sprache: Englisch
• ISBN-10: 3030128539
• ISBN-13: 9783030128531
• Einband: Hardcover
• Auflage: 1. Auflage
• Zustand: new

Über diesen Titel

Inhaltsangabe

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.