'Misclassification error' greedy heuristic to construct decision trees for inconsistent decision tables

Handle URI:
http://hdl.handle.net/10754/564871
Title:
'Misclassification error' greedy heuristic to construct decision trees for inconsistent decision tables
Authors:
Azad, Mohammad ( 0000-0001-9851-1420 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
A greedy algorithm has been presented in this paper to construct decision trees for three different approaches (many-valued decision, most common decision, and generalized decision) in order to handle the inconsistency of multiple decisions in a decision table. In this algorithm, a greedy heuristic ‘misclassification error’ is used which performs faster, and for some cost function, results are better than ‘number of boundary subtables’ heuristic in literature. Therefore, it can be used in the case of larger data sets and does not require huge amount of memory. Experimental results of depth, average depth and number of nodes of decision trees constructed by this algorithm are compared in the framework of each of the three approaches.
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:
Scitepress
Journal:
Proceedings of the International Conference on Knowledge Discovery and Information Retrieval
Conference/Event name:
6th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2014
Issue Date:
2014
DOI:
10.5220/0005059201840191
Type:
Conference Paper
ISBN:
9789897580482
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.accessioned2015-08-04T07:23:41Zen
dc.date.available2015-08-04T07:23:41Zen
dc.date.issued2014en
dc.identifier.isbn9789897580482en
dc.identifier.doi10.5220/0005059201840191en
dc.identifier.urihttp://hdl.handle.net/10754/564871en
dc.description.abstractA greedy algorithm has been presented in this paper to construct decision trees for three different approaches (many-valued decision, most common decision, and generalized decision) in order to handle the inconsistency of multiple decisions in a decision table. In this algorithm, a greedy heuristic ‘misclassification error’ is used which performs faster, and for some cost function, results are better than ‘number of boundary subtables’ heuristic in literature. Therefore, it can be used in the case of larger data sets and does not require huge amount of memory. Experimental results of depth, average depth and number of nodes of decision trees constructed by this algorithm are compared in the framework of each of the three approaches.en
dc.publisherScitepressen
dc.title'Misclassification error' greedy heuristic to construct decision trees for inconsistent 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 International Conference on Knowledge Discovery and Information Retrievalen
dc.conference.date21 October 2014 through 24 October 2014en
dc.conference.name6th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2014en
kaust.authorAzad, Mohammaden
kaust.authorMoshkov, Mikhailen
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