Results 11 to 20 of about 3,458,167 (137)

Weighted Depth of Deterministic and Nondeterministic Decision Trees for Recognition Properties of Decision Rule Systems

Procedia Computer Science
Decision 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

MR Derived Cardiac Metabolism Changes in Patients With Obesity and Diabetes: Knowledge Discovery Via Bayesian Networks and Random Forest Classification [PDF]

NMR Biomed
Characterization of healthy vs. diabetic (73:67$$ 73:67 $$) random forest classification group and its Bayesian network learned through NOTEARS structure algorithm on multi‐modal data. Shown computed SHapley Additive exPlanation (SHAP) values for various features.
Hanninger I, Krafčík A, Levelt E, J Rayner J, T Rodgers C, Neubauer S, Grau V, J Rider O, Valkovič L.   +8 more
europepmc   +2 more sources

On Depth of Deterministic and Nondeterministic Decision Trees for Decision Rule Systems from Closed Classes

Procedia Computer Science
The 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

Revisiting Trace and Testing Equivalences for Nondeterministic and Probabilistic Processes [PDF]

, 2012
One of the most studied extensions of testing theory to nondeterministic and probabilistic processes yields unrealistic probabilities estimations that give rise to two anomalies. First, probabilistic testing equivalence does not imply probabilistic trace
Nicola Michele Loreti, Rocco Nicola, De Nicola, Rocco, Marco Bernardo, Rocco De, Loreti, Michele, Rocco De Nicola, Michele Loreti, Bernardo, Marco, De Nicola, R   +9 more
core   +1 more source

Inducing decision trees with an ant colony optimization algorithm [PDF]

, 2012
Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction ...
Otero, Fernando E.B., Johnson, Colin G., Freitas, Alex A.   +2 more
core   +1 more source

Comparative Analysis of Deterministic and Nondeterministic Decision Trees for Decision Tables from Closed Classes [PDF]

, 2023
In this paper, we consider classes of decision tables with many-valued decisions closed under operations of removal of columns, changing of decisions, permutation of columns, and duplication of columns.
Moshkov, Mikhail, Ostonov, Azimkhon
core   +1 more source

Alternating model trees [PDF]

, 2015
Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes.
Kramer, Stefan, Eibe Frank, Michael Mayo, Mayo, Michael, Frank, Eibe, Stefan Kramer   +5 more
core   +1 more source

Separating complexity classes related to Ω-decision trees [PDF]

, 1992
By proving exponential lower and polynomial upper bounds for parity decision trees and collecting similar bounds for nondeterministic and co-nondeterministic decision trees, the complexity classes related to polynomial-size deterministic ...
Carsten Damm, Damm, Carsten, Meinel, Christoph, Christoph Meinel   +3 more
core   +1 more source

Three Problems for Decision Rule Systems from Closed Classes

Axioms
The study of the relationships between DRSs (Decision Rule Systems) and DTs (Decision Trees) is of considerable interest in computer science. In this paper, we consider classes of DRSs that are closed under specific operations.
Kerven Durdymyradov, Mikhail Moshkov
doaj   +1 more source

Utilizing fuzzy decision trees in decision making

, 2008
The seminal work of Zadeh (1965), namely fuzzy set theory (FST), has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity.
Beynon, Malcolm James, Malcolm J. Beynonm   +1 more
core   +1 more source