3 research outputs found
Bounds on Depth of Decision Trees Derived from Decision Rule Systems
Systems of decision rules and decision trees are widely used as a means for
knowledge representation, as classifiers, and as algorithms. They are among the
most interpretable models for classifying and representing knowledge. The study
of relationships between these two models is an important task of computer
science. It is easy to transform a decision tree into a decision rule system.
The inverse transformation is a more difficult task. In this paper, we study
unimprovable upper and lower bounds on the minimum depth of decision trees
derived from decision rule systems depending on the various parameters of these
systems
Aggregation of biological knowledge for immunological and virological applications
Ph.DDOCTOR OF PHILOSOPH
A Rules-to-Trees Conversion in the Inductive Database System VINLEN
Abstract. Decision trees and rules are completing methods of knowledge representation. Both have advantages in some applications. Algorithms that convert trees to rules are common. In the paper an algorithm that converts rules to decision tree and its implementation in inductive database VINLEN is presented