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Comparative Analysis of Deterministic and Nondeterministic Decision Trees

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Comparative Analysis of Deterministic and Nondeterministic Decision Trees/ by Mikhail Moshkov.
作者:	Moshkov, Mikhail.
面頁冊數:	XVI, 297 p. 4 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Data Engineering. -
電子資源:	https://doi.org/10.1007/978-3-030-41728-4
ISBN:	9783030417284

Comparative Analysis of Deterministic and Nondeterministic Decision Trees
Moshkov, Mikhail.

Comparative Analysis of Deterministic and Nondeterministic Decision Trees[electronic resource] /by Mikhail Moshkov. - 1st ed. 2020. - XVI, 297 p. 4 illus.online resource. - Intelligent Systems Reference Library,1791868-4394 ;. - Intelligent Systems Reference Library,67.

Introduction -- Basic Deﬁnitions and Notation -- Lower Bounds on Complexity of Deterministic Decision Trees for Decision Tables -- Upper Bounds on Complexity and Algorithms for Construction of Deterministic Decision Trees for Decision Tables -- Bounds on Complexity and Algorithms for Construction of Nondeterministic and Strongly Nondeterministic Decision Trees for Decision Tables -- Closed Classes of Boolean Functions -- Algorithmic Problems -- Basic Deﬁnitions and Notation -- Main Reductions -- Functions on Main Diagonal and Below -- Local Upper Types of Restricted Sccf-Triples -- Bounds Inside Types. .

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. .

ISBN: 9783030417284

Standard No.: 10.1007/978-3-030-41728-4doiSubjects--Topical Terms:

1226308
Data Engineering.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Comparative Analysis of Deterministic and Nondeterministic Decision Trees
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505 0 $a Introduction -- Basic Deﬁnitions and Notation -- Lower Bounds on Complexity of Deterministic Decision Trees for Decision Tables -- Upper Bounds on Complexity and Algorithms for Construction of Deterministic Decision Trees for Decision Tables -- Bounds on Complexity and Algorithms for Construction of Nondeterministic and Strongly Nondeterministic Decision Trees for Decision Tables -- Closed Classes of Boolean Functions -- Algorithmic Problems -- Basic Deﬁnitions and Notation -- Main Reductions -- Functions on Main Diagonal and Below -- Local Upper Types of Restricted Sccf-Triples -- Bounds Inside Types. .
520 $a 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. .
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