Optimization of Decision Trees with Hypotheses for Knowledge Representation

Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this paper, we consider decision trees that use two types of queries: queries based on one attribute each and queries based on hypotheses about values of all attributes. Such decision trees are similar to the ones studied in exact learning, where membership and equivalence queries are allowed. We present dynamic programming algorithms for minimization of the depth and number of nodes of above decision trees and discuss results of computer experiments on various data sets and randomly generated Boolean functions. Decision trees with hypotheses generally have less complexity, i.e., they are more understandable and more suitable as a means for knowledge representation.
Original languageEnglish (US)
Pages (from-to)1580
JournalElectronics
Volume10
Issue number13
DOIs
StatePublished - Jun 30 2021

Fingerprint

Dive into the research topics of 'Optimization of Decision Trees with Hypotheses for Knowledge Representation'. Together they form a unique fingerprint.

Cite this