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100 1 _aDurdymyradov, Kerven.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aDecision Trees Versus Systems of Decision Rules
_h[electronic resource] :
_bA Rough Set Approach /
_cby Kerven Durdymyradov, Mikhail Moshkov, Azimkhon Ostonov.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIV, 307 p. 50 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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_2rda
490 1 _aStudies in Big Data,
_x2197-6511 ;
_v160
505 0 _aIntroduction -- Problems Over Information Systems -- Comparative Analysis of Deterministic and Nondeterministic Decision Tree Complexity Global Approach -- Comparative Analysis of Deterministic and Nondeterministic Decision Tree Complexity Local Approach -- Time and Space Complexity of Deterministic and Nondeterministic Decision Trees Global Approach -- Time and Space Complexity of Deterministic and Nondeterministic Decision Trees Local Approach -- Decision Tables from Closed Classes -- Comparative Analysis of Deterministic and Nondeterministic Decision Trees for Decision Tables from Closed Classes -- Complexity of Deterministic and Nondeterministic Decision Trees for Decision Tables with Many-valued Decisions from Closed Classes -- Complexity of Deterministic and Nondeterministic Decision Trees for Conventional Decision Tables from Closed Classes -- Complexity of Deterministic and Strongly Nondeterministic Decision Trees for Decision Tables with 0 1 Decisions from Closed Classes -- Recognition and Membership Problems for Formal Languages -- Decision Trees for Binary Subword closed Languages -- Transforming Decision Rule Systems into Deterministic Decision Trees -- Bounds on Depth of Decision Trees Derived from Decision Rule Systems -- Construction of Decision Trees and Acyclic Decision Graphs from Decision Rule Systems.
520 _aThis book explores, within the framework of rough set theory, the complexity of decision trees and decision rule systems and the relationships between them for problems over information systems, for decision tables from closed classes, and for problems involving formal languages. Decision trees and systems of decision rules are widely used as means of representing knowledge, as classifiers that predict decisions for new objects, as well as algorithms for solving various problems of fault diagnosis, combinatorial optimization, etc. Decision trees and systems of decision rules are among the most interpretable models of knowledge representation and classification. Investigating the relationships between these two models is an important task in computer science. The possibilities of transforming decision rule systems into decision trees are being studied in detail. The results are useful for researchers using decision trees and decision rule systems in data analysis, especially in rough set theory, logical analysis of data, and test theory. This book is also used to create courses for graduate students.
541 _fUABC ;
_cPerpetuidad
650 0 _aComputational intelligence.
650 0 _aArtificial intelligence.
650 1 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence.
700 1 _aMoshkov, Mikhail.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aOstonov, Azimkhon.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031715853
776 0 8 _iPrinted edition:
_z9783031715877
776 0 8 _iPrinted edition:
_z9783031715884
830 0 _aStudies in Big Data,
_x2197-6511 ;
_v160
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-71586-0
912 _aZDB-2-INR
912 _aZDB-2-SXIT
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