Classification and Optimization of Decision Trees for Inconsistent Decision Tables Represented as MVD Tables

Handle URI:
http://hdl.handle.net/10754/583064
Title:
Classification and Optimization of Decision Trees for Inconsistent Decision Tables Represented as MVD Tables
Authors:
Azad, Mohammad ( 0000-0001-9851-1420 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
Decision tree is a widely used technique to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples (objects) with equal values of conditional attributes but different decisions (values of the decision attribute), then to discover the essential patterns or knowledge from the data set is challenging. We consider three approaches (generalized, most common and many-valued decision) to handle such inconsistency. We created different greedy algorithms using various types of impurity and uncertainty measures to construct decision trees. We compared the three approaches based on the decision tree properties of the depth, average depth and number of nodes. Based on the result of the comparison, we choose to work with the many-valued decision approach. Now to determine which greedy algorithms are efficient, we compared them based on the optimization and classification results. It was found that some greedy algorithms Mult\_ws\_entSort, and Mult\_ws\_entML are good for both optimization and classification.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Polish Information Processing Society PTI
Journal:
Proceedings of the 2015 Federated Conference on Computer Science and Information Systems
Conference/Event name:
Annals of Computer Science and Information Systems, Volume 5
Issue Date:
11-Oct-2015
DOI:
10.15439/2015F231
Type:
Conference Paper
Additional Links:
https://fedcsis.org/proceedings/2015/drp/231.html
Appears in Collections:
Conference Papers; 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.accessioned2015-12-01T13:56:29Zen
dc.date.available2015-12-01T13:56:29Zen
dc.date.issued2015-10-11en
dc.identifier.doi10.15439/2015F231en
dc.identifier.urihttp://hdl.handle.net/10754/583064en
dc.description.abstractDecision tree is a widely used technique to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples (objects) with equal values of conditional attributes but different decisions (values of the decision attribute), then to discover the essential patterns or knowledge from the data set is challenging. We consider three approaches (generalized, most common and many-valued decision) to handle such inconsistency. We created different greedy algorithms using various types of impurity and uncertainty measures to construct decision trees. We compared the three approaches based on the decision tree properties of the depth, average depth and number of nodes. Based on the result of the comparison, we choose to work with the many-valued decision approach. Now to determine which greedy algorithms are efficient, we compared them based on the optimization and classification results. It was found that some greedy algorithms Mult\_ws\_entSort, and Mult\_ws\_entML are good for both optimization and classification.en
dc.publisherPolish Information Processing Society PTIen
dc.relation.urlhttps://fedcsis.org/proceedings/2015/drp/231.htmlen
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.titleClassification and Optimization of Decision Trees for Inconsistent Decision Tables Represented as MVD Tablesen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalProceedings of the 2015 Federated Conference on Computer Science and Information Systemsen
dc.conference.dateSeptember 13–16, 2015en
dc.conference.nameAnnals of Computer Science and Information Systems, Volume 5en
dc.conference.locationŁódź, Polanden
dc.eprint.versionPost-printen
kaust.authorAzad, Mohammaden
kaust.authorMoshkov, Mikhailen
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