Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Intelligent Systems Reference Library, 156
Release date:	March 2019
First published:	2020
Authors:	Fawaz Alsolami • Mohammad Azad • Igor Chikalov • Mikhail Moshkov
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	276
Edition:	1st ed. 2020
ISBN-13:	978-3-03-012853-1
Categories:	Books > Science & Mathematics > Mathematics > Optimization > General Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-012853-9
Barcode:	9783030128531

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