Author(s)

Kerven Durdymyradov, Mikhail Moshkov

Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

We consider various tasks of recognizing properties of DRSs (Decision Rule Systems) in this paper. As solution algorithms, DDTs (Deterministic Decision Trees) and NDTs (Nondeterministic Decision Trees) are used. An NDT can be considered as a representation of a DRS that satisfies the conditions of the considered task and covers all potential inputs. It has been shown that the minimum depth of a DDT solving the task does not exceed the square of the minimum depth of an NDT. The growth of the minimum number of nodes in DDTs and NDTs can be exponential with the size of the original DRSs. Therefore, in the general case, it is better to simulate the behavior of the DT (Decision Tree) on the given tuple of feature values rather than building the entire tree. We propose a greedy algorithm for such modeling and study its efficiency for a class of tasks of recognizing properties of DRSs. The obtained results may be of interest for data analysis in which both DRSs and DTs are intensively studied. In particular, these results make one think about the possibilities of transforming DRSs into DTs.

Deterministic Decision Tree, Nondeterministic Decision Tree, Decision Rule System

Durdymyradov, K. and Moshkov, M. (2025) Recognizing Properties of Decision Rule Systems Using Deterministic and Nondeterministic Decision Trees. Journal of Intelligent Learning Systems and Applications, 17, 193-210. doi: 10.4236/jilsa.2025.173013.