Comparative Analysis of Deterministic and Nondeterministic Decision Trees



de

Éditeur :

Springer


Paru le : 2020-03-14



eBook Téléchargement , DRM LCP 🛈 DRM Adobe 🛈
Lecture en ligne (streaming)
94,94

Téléchargement immédiat
Dès validation de votre commande
Ajouter à ma liste d'envies
Image Louise Reader présentation

Louise Reader

Lisez ce titre sur l'application Louise Reader.

Description

This book compares four parameters of problems in arbitrary information systems: complexity of problem representation and complexity of deterministic, nondeterministic, and strongly nondeterministic decision trees for problem solving. Deterministic decision trees are widely used as classifiers, as a means of knowledge representation, and as algorithms. Nondeterministic (strongly nondeterministic) decision trees can be interpreted as systems of true decision rules that cover all objects (objects from one decision class).
 
This book develops tools for the study of decision trees, including bounds on complexity and algorithms for construction of decision trees for decision tables with many-valued decisions. It considers two approaches to the investigation of decision trees for problems in information systems: local, when decision trees can use only attributes from the problem representation; and global, when decision trees can use arbitrary attributes from the information system. For both approaches, it describes all possible types of relationships among the four parameters considered and discusses the algorithmic problems related to decision tree optimization. The results presented are useful for researchers who apply decision trees and rules to algorithm design and to data analysis, especially those working in rough set theory, test theory and logical analysis of data. This book can also be used as the basis for graduate courses. 
Pages
297 pages
Collection
n.c
Parution
2020-03-14
Marque
Springer
EAN papier
9783030417277
EAN PDF
9783030417284

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
29
Taille du fichier
4905 Ko
Prix
94,94 €
EAN EPUB
9783030417284

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
29
Taille du fichier
46633 Ko
Prix
94,94 €

Suggestions personnalisées