rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Comparative Analysis of Deterministic and Nondeterministic Decision Trees
  name:
    name part: Moshkov, Mikhail.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2020
    edition: 1st ed. 2020.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XVI, 297 pages 4 illustrations)
  abstract: 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. .
  table of contents: 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. .
  note: by Mikhail Moshkov.
  subject:
    topic: Computational intelligence
  subject:
    topic: Control engineering
  subject:
    topic: Engineering-Data processing
  subject:
    topic: Computational Intelligence
  subject:
    topic: Control and Systems Theory
  subject:
    topic: Data Engineering
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Intelligent Systems Reference Library, 179
  identifier: 9783030417284
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 200313
    record change date: 20230330185427.1
    record identifier: in00004381134