Minimization of decision tree depth for multi-label decision tables

Handle URI:
http://hdl.handle.net/10754/565000
Title:
Minimization of decision tree depth for multi-label decision tables
Authors:
Azad, Mohammad ( 0000-0001-9851-1420 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
In this paper, we consider multi-label decision tables that have a set of decisions attached to each row. Our goal is to find one decision from the set of decisions for each row by using decision tree as our tool. Considering our target to minimize the depth of the decision tree, we devised various kinds of greedy algorithms as well as dynamic programming algorithm. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of depth of decision trees.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2014 IEEE International Conference on Granular Computing (GrC)
Conference/Event name:
2014 IEEE International Conference on Granular Computing, GrC 2014
Issue Date:
Oct-2014
DOI:
10.1109/GRC.2014.6982798
Type:
Conference Paper
ISBN:
9781479954643
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAzad, Mohammaden
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2015-08-04T07:27:44Zen
dc.date.available2015-08-04T07:27:44Zen
dc.date.issued2014-10en
dc.identifier.isbn9781479954643en
dc.identifier.doi10.1109/GRC.2014.6982798en
dc.identifier.urihttp://hdl.handle.net/10754/565000en
dc.description.abstractIn this paper, we consider multi-label decision tables that have a set of decisions attached to each row. Our goal is to find one decision from the set of decisions for each row by using decision tree as our tool. Considering our target to minimize the depth of the decision tree, we devised various kinds of greedy algorithms as well as dynamic programming algorithm. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of depth of decision trees.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectdecision treeen
dc.subjectdepthen
dc.subjectdynamic programmingen
dc.subjectgreedy algorithmen
dc.subjectmulti-label decision tableen
dc.titleMinimization of decision tree depth for multi-label decision tablesen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Groupen
dc.identifier.journal2014 IEEE International Conference on Granular Computing (GrC)en
dc.conference.date22 October 2014 through 24 October 2014en
dc.conference.name2014 IEEE International Conference on Granular Computing, GrC 2014en
kaust.authorAzad, Mohammaden
kaust.authorMoshkov, Mikhailen
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