Modeling the Functioning of Decision Trees Based on Decision Rule Systems by Greedy Algorithm

DRSs (Decision Rule Systems) and DTs (Decision Trees) are well known as classification tools, knowledge representation methods, and algorithms. Their clarity and ease of interpretation in data analysis are widely recognized. The study of the relationship between DTs and DRSs is an important problem in computer science. There are established methods for converting DTs to DRSs. In this work, we explore the inverse transformation problem, which is challenging. Rather than constructing a full DT, our research focuses on a greedy algorithm that simulates the functioning of a DT for an input array of feature values.