Minimizing size of decision trees for multi-label decision tables

Handle URI:
http://hdl.handle.net/10754/564997
Title:
Minimizing size of decision trees for multi-label decision tables
Authors:
Azad, Mohammad ( 0000-0001-9851-1420 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
We used decision tree as a model to discover the knowledge from multi-label decision tables where each row has a set of decisions attached to it and our goal is to find out one arbitrary decision from the set of decisions attached to a row. The size of the decision tree can be small as well as very large. We study here different greedy as well as dynamic programming algorithms to minimize the size of the decision trees. When we compare the optimal result from dynamic programming algorithm, we found some greedy algorithms produce results which are close to the optimal result for the minimization of number of nodes (at most 18.92% difference), number of nonterminal nodes (at most 20.76% difference), and number of terminal nodes (at most 18.71% difference).
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:
Polish Information Processing Society PTI
Journal:
Proceedings of the 2014 Federated Conference on Computer Science and Information Systems
Conference/Event name:
2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014
Issue Date:
29-Sep-2014
DOI:
10.15439/2014F256
Type:
Conference Paper
ISBN:
9788360810583
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.accessioned2015-08-04T07:27:38Zen
dc.date.available2015-08-04T07:27:38Zen
dc.date.issued2014-09-29en
dc.identifier.isbn9788360810583en
dc.identifier.doi10.15439/2014F256en
dc.identifier.urihttp://hdl.handle.net/10754/564997en
dc.description.abstractWe used decision tree as a model to discover the knowledge from multi-label decision tables where each row has a set of decisions attached to it and our goal is to find out one arbitrary decision from the set of decisions attached to a row. The size of the decision tree can be small as well as very large. We study here different greedy as well as dynamic programming algorithms to minimize the size of the decision trees. When we compare the optimal result from dynamic programming algorithm, we found some greedy algorithms produce results which are close to the optimal result for the minimization of number of nodes (at most 18.92% difference), number of nonterminal nodes (at most 20.76% difference), and number of terminal nodes (at most 18.71% difference).en
dc.publisherPolish Information Processing Society PTIen
dc.titleMinimizing size of decision trees 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.journalProceedings of the 2014 Federated Conference on Computer Science and Information Systemsen
dc.conference.date7 September 2014 through 10 September 2014en
dc.conference.name2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014en
kaust.authorAzad, Mohammaden
kaust.authorMoshkov, Mikhailen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.