Results 11 to 20 of about 3,823,341 (178)
Proof complexity of systems of (non-deterministic) decision trees and branching programs [PDF]
CoRR, 2019 36 pages, 1 figure, full version of CSL 2020 ...
Sam Buss +2 more
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Complexity of Deterministic and Strongly Nondeterministic Decision Trees for Decision Tables from Closed Classes [PDF]
IEEE Access, 2023 This paper investigates classes of decision tables (DTs) with 0-1-decisions that are closed under the removal of attributes (columns) and changes to the assigned decisions to rows. For tables from any closed class (CC), the authors examine how the minimum complexity of deterministic decision trees (DDTs) depends on the minimum complexity of a strongly ...
Azimkhon Ostonov, Mikhail Moshkov
openaire +4 more sources
A Local Approach to Studying the Time and Space Complexity of Deterministic and Nondeterministic Decision Trees [PDF]
CoRR, 2023 In this paper, we study arbitrary infinite binary information systems each of which consists of an infinite set called universe and an infinite set of two-valued functions (attributes) defined on the universe. We consider the notion of a problem over information system, which is described by a finite number of attributes and a mapping associating a ...
Kerven Durdymyradov, Mikhail Moshkov
core +6 more sources
Comparison of Deterministic and Nondeterministic Decision Trees for Decision Tables with Many-valued Decisions from Closed Classes [PDF]
CoRR, 2023 arXiv admin note: substantial text overlap with arXiv:2305.06093, arXiv:2304 ...
Azimkhon Ostonov, Mikhail Moshkov
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AxiomsIn this paper, an efficient algorithm for modeling the operation of a DDT (Deterministic Decision Tree) solving the problem of realizability of DRs (Decision Rules) is proposed and analyzed.
Kerven Durdymyradov, Mikhail Moshkov
doaj +2 more sources
C-Net: A Method for Generating Non-deterministic and Dynamic Multivariate Decision Trees [PDF]
Knowledge and Information Systems, 2001 Despite the fact that artificial neural networks (ANNs) are universal function approximators, their black box nature (that is, their lack of direct interpretability or expressive power) limits their utility. In contrast, univariate decision trees (UDTs) have expressive power, although usually they are not as accurate as ANNs. We propose an improvement,
Hussein A. Abbass +2 more
openaire +5 more sources
Procedia Computer ScienceDecision rule systems and decision trees are frequently used in computer science as interpretable models. Understanding their complexity in terms of attribute costs is crucial when decisions must be made with minimum resource usage.
Kerven Durdymyradov, Mikhail Moshkov
openaire +3 more sources
Procedia Computer ScienceThe study of the relationships between decision rule systems and decision trees is of considerable interest in computer science. These models are among the most interpretable tools used in knowledge representation, classification, and decision-making. In
Kerven Durdymyradov, Mikhail Moshkov
openaire +3 more sources
Randomized versus Deterministic Decision Tree Size
Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023 A classic result of Nisan [SICOMP '91] states that the deterministic decision tree∗depth∗complexity of every total Boolean function is at most the cube of its randomized decision tree∗depth∗complexity. The question whether randomness helps in significantly reducing the∗size∗of decision trees appears not to have been addressed.
Arkadev Chattopadhyay +4 more
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Decision trees (DTRs) and decision rules are extensively examined and applied in various domains of computer science. The theory of DTRs and rules highlights several crucial inquiries, such as how the complexity of deterministic decision trees (DDTRs ...
Azimkhon Ostonov, Mikhail Moshkov
openaire +3 more sources