Representation of knowledge by decision trees for decision tables with multiple decisions

Mohammad Azad, Igor Chikalov, Mikhail Moshkov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we study decisions trees for decision tables with multiple decisions as a means for knowledge representation. To this end, we consider three methods to design decision trees and evaluate the number of nodes, and local and global misclassification rates of constructed trees. The considered methods are based on a dynamic programming algorithm for bi-objective optimization of decision trees. The goal of this study is to construct trees with reasonable number of nodes and at the same time reasonable accuracy. Previously, it was mentioned that the consideration of only the global misclassification rate of the decision tree is not enough and it is necessary to study also the local misclassification rate. The reason is that even if the global misclassification rate related to the whole tree is enough small, the local misclassification rate related to the terminal nodes of the tree can be too big. One of the considered methods allows us to construct the decision trees with moderate number of nodes as well as moderate global and local misclassification rates. These decision trees can be used for the knowledge representation.
Original languageEnglish (US)
Title of host publicationProcedia Computer Science
PublisherElsevier BV
Pages653-659
Number of pages7
DOIs
StatePublished - Oct 2 2020

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