Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions

dc.contributor.authorAlsolami, Fawaz
dc.contributor.authorAzad, Mohammad
dc.contributor.authorChikalov, Igor
dc.contributor.authorMoshkov, Mikhail
dc.contributor.departmentComputer Science Program
dc.contributor.departmentOffice of the VP
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.date.accessioned2019-11-18T09:32:45Z
dc.date.available2019-11-18T09:32:45Z
dc.date.issued2019-05-21
dc.date.published-online2019-05-21
dc.date.published-print2020
dc.description.abstractThe results presented here (including the assessment of a new tool – inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions. The authors consider various examples of problems and corresponding decision tables with many-valued decisions, discuss the difference between decision and inhibitory trees and rules, and develop tools for their analysis and design. Applications include the study of totally optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory trees and rules; the comparison of greedy heuristics for tree and rule construction as single-criterion and bi-criteria optimization algorithms; and the development of a restricted multi-pruning approach used in classification and knowledge representation.
dc.identifier.citationAlsolami, F., Azad, M., Chikalov, I., & Moshkov, M. (2020). Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions. Intelligent Systems Reference Library. doi:10.1007/978-3-030-12854-8
dc.identifier.doi10.1007/978-3-030-12854-8
dc.identifier.isbn9783030128531
dc.identifier.isbn9783030128548
dc.identifier.urihttp://hdl.handle.net/10754/660089
dc.publisherSpringer Nature
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_1
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_2
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_3
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_4
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_5
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_6
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_7
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_8
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_9
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_10
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_11
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_12
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_13
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_14
dc.relation.haspartDOI:10.1007/978-3-030-12854-8_15
dc.relation.urlhttp://link.springer.com/10.1007/978-3-030-12854-8
dc.subjectIntelligent Systems
dc.subjectDecision Trees
dc.subjectInhibitory Trees
dc.subjectDecision Tables
dc.subjectDynamic Programming
dc.titleDecision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions
dc.typeBook
display.details.left<span><h5>Type</h5>Book<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0001-5858-4908&spc.sf=dc.date.issued&spc.sd=DESC">Alsolami, Fawaz</a> <a href="https://orcid.org/0000-0001-5858-4908" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0001-9851-1420&spc.sf=dc.date.issued&spc.sd=DESC">Azad, Mohammad</a> <a href="https://orcid.org/0000-0001-9851-1420" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Chikalov, Igor,equals">Chikalov, Igor</a><br><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0003-0085-9483&spc.sf=dc.date.issued&spc.sd=DESC">Moshkov, Mikhail</a> <a href="https://orcid.org/0000-0003-0085-9483" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><br><h5>KAUST Department</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Computer Science Program,equals">Computer Science Program</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Office of the VP,equals">Office of the VP</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Computational Bioscience Research Center (CBRC),equals">Computational Bioscience Research Center (CBRC)</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division,equals">Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Applied Mathematics and Computational Science Program,equals">Applied Mathematics and Computational Science Program</a><br><br><h5>Online Publication Date</h5>2019-05-21<br><br><h5>Print Publication Date</h5>2020<br><br><h5>Date</h5>2019-05-21</span>
display.details.right<span><h5>Abstract</h5>The results presented here (including the assessment of a new tool – inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions. The authors consider various examples of problems and corresponding decision tables with many-valued decisions, discuss the difference between decision and inhibitory trees and rules, and develop tools for their analysis and design. Applications include the study of totally optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory trees and rules; the comparison of greedy heuristics for tree and rule construction as single-criterion and bi-criteria optimization algorithms; and the development of a restricted multi-pruning approach used in classification and knowledge representation.<br><br><h5>Citation</h5>Alsolami, F., Azad, M., Chikalov, I., & Moshkov, M. (2020). Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions. Intelligent Systems Reference Library. doi:10.1007/978-3-030-12854-8<br><br><h5>Publisher</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.publisher=Springer Nature,equals">Springer Nature</a><br><br><h5>DOI</h5><a href="https://doi.org/10.1007/978-3-030-12854-8">10.1007/978-3-030-12854-8</a><br><br><h5>Additional Links</h5>http://link.springer.com/10.1007/978-3-030-12854-8</span>
kaust.personAlsolami, Fawaz
kaust.personAzad, Mohammad
kaust.personChikalov, Igor
kaust.personMoshkov, Mikhail
orcid.id0000-0003-0085-9483
orcid.id0000-0001-9851-1420
orcid.id0000-0001-5858-4908
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