Optimization of Decision Trees with Hypotheses for Knowledge Representation

Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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
Issue number13
StatePublished - Jun 30 2021


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