Optimization of Decision Trees with Hypotheses for Knowledge Representation
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/13/1580 |
| _version_ | 1797528176270770176 |
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| author | Mohammad Azad Igor Chikalov Shahid Hussain Mikhail Moshkov |
| author_facet | Mohammad Azad Igor Chikalov Shahid Hussain Mikhail Moshkov |
| author_sort | Mohammad Azad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:54:27Z |
| format | Article |
| id | doaj.art-172559a10f5144b5ac925545e9528ea5 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T09:54:27Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-172559a10f5144b5ac925545e9528ea52023-11-22T02:27:40ZengMDPI AGElectronics2079-92922021-06-011013158010.3390/electronics10131580Optimization of Decision Trees with Hypotheses for Knowledge RepresentationMohammad Azad0Igor Chikalov1Shahid Hussain2Mikhail Moshkov3Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72441, Saudi ArabiaIntel Corporation, 5000 W Chandler Blvd, Chandler, AZ 85226, USAComputer Science Program, Dhanani School of Science and Engineering, Habib University, Karachi 75290, PakistanComputer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi ArabiaIn 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.https://www.mdpi.com/2079-9292/10/13/1580knowledge representationdecision treehypothesisdepthnumber of nodes |
| spellingShingle | Mohammad Azad Igor Chikalov Shahid Hussain Mikhail Moshkov Optimization of Decision Trees with Hypotheses for Knowledge Representation Electronics knowledge representation decision tree hypothesis depth number of nodes |
| title | Optimization of Decision Trees with Hypotheses for Knowledge Representation |
| title_full | Optimization of Decision Trees with Hypotheses for Knowledge Representation |
| title_fullStr | Optimization of Decision Trees with Hypotheses for Knowledge Representation |
| title_full_unstemmed | Optimization of Decision Trees with Hypotheses for Knowledge Representation |
| title_short | Optimization of Decision Trees with Hypotheses for Knowledge Representation |
| title_sort | optimization of decision trees with hypotheses for knowledge representation |
| topic | knowledge representation decision tree hypothesis depth number of nodes |
| url | https://www.mdpi.com/2079-9292/10/13/1580 |
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