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Optimization of Decision Trees with Hypotheses for Knowledge Representation

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Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72441, Saudi Arabia
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Intel Corporation, 5000 W Chandler Blvd, Chandler, AZ 85226, USA
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Computer Science Program, Dhanani School of Science and Engineering, Habib University, Karachi 75290, Pakistan
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Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Author to whom correspondence should be addressed.
Academic Editors: Dimitris Apostolou and Dionisios Sotiropoulos
Electronics 2021, 10(13), 1580; https://doi.org/10.3390/electronics10131580
Received: 26 May 2021 / Revised: 23 June 2021 / Accepted: 28 June 2021 / Published: 30 June 2021
(This article belongs to the Special Issue AI-Based Knowledge Management)
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. View Full-Text
Keywords: knowledge representation; decision tree; hypothesis; depth; number of nodes knowledge representation; decision tree; hypothesis; depth; number of nodes
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MDPI and ACS Style

Azad, M.; Chikalov, I.; Hussain, S.; Moshkov, M. Optimization of Decision Trees with Hypotheses for Knowledge Representation. Electronics 2021, 10, 1580. https://doi.org/10.3390/electronics10131580

AMA Style

Azad M, Chikalov I, Hussain S, Moshkov M. Optimization of Decision Trees with Hypotheses for Knowledge Representation. Electronics. 2021; 10(13):1580. https://doi.org/10.3390/electronics10131580

Chicago/Turabian Style

Azad, Mohammad, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov. 2021. "Optimization of Decision Trees with Hypotheses for Knowledge Representation" Electronics 10, no. 13: 1580. https://doi.org/10.3390/electronics10131580

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