Classification for Inconsistent Decision Tables

Handle URI:
http://hdl.handle.net/10754/622182
Title:
Classification for Inconsistent Decision Tables
Authors:
Azad, Mohammad ( 0000-0001-9851-1420 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
Decision trees have been used widely to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples with equal values of conditional attributes but different labels, then to discover the essential patterns or knowledge from the data set is challenging. Three approaches (generalized, most common and many-valued decision) have been considered to handle such inconsistency. The decision tree model has been used to compare the classification results among three approaches. Many-valued decision approach outperforms other approaches, and M_ws_entM greedy algorithm gives faster and better prediction accuracy.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Azad M, Moshkov M (2016) Classification for Inconsistent Decision Tables. Lecture Notes in Computer Science: 525–534. Available: http://dx.doi.org/10.1007/978-3-319-47160-0_48.
Publisher:
Springer Nature
Journal:
Lecture Notes in Computer Science
Conference/Event name:
International Joint Conference on Rough Sets, IJCRS 2016
Issue Date:
28-Sep-2016
DOI:
10.1007/978-3-319-47160-0_48
Type:
Conference Paper
ISSN:
0302-9743; 1611-3349
Additional Links:
http://link.springer.com/chapter/10.1007%2F978-3-319-47160-0_48
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.accessioned2017-01-02T08:42:36Z-
dc.date.available2017-01-02T08:42:36Z-
dc.date.issued2016-09-28en
dc.identifier.citationAzad M, Moshkov M (2016) Classification for Inconsistent Decision Tables. Lecture Notes in Computer Science: 525–534. Available: http://dx.doi.org/10.1007/978-3-319-47160-0_48.en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.doi10.1007/978-3-319-47160-0_48en
dc.identifier.urihttp://hdl.handle.net/10754/622182-
dc.description.abstractDecision trees have been used widely to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples with equal values of conditional attributes but different labels, then to discover the essential patterns or knowledge from the data set is challenging. Three approaches (generalized, most common and many-valued decision) have been considered to handle such inconsistency. The decision tree model has been used to compare the classification results among three approaches. Many-valued decision approach outperforms other approaches, and M_ws_entM greedy algorithm gives faster and better prediction accuracy.en
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/chapter/10.1007%2F978-3-319-47160-0_48en
dc.subjectDecision treesen
dc.subjectGreedy algorithmsen
dc.subjectClassificationsen
dc.subjectMany-valued decisionsen
dc.subjectInconsistent decision tablesen
dc.titleClassification for Inconsistent Decision Tablesen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalLecture Notes in Computer Scienceen
dc.conference.date2016-10-07 to 2016-10-11en
dc.conference.nameInternational Joint Conference on Rough Sets, IJCRS 2016en
dc.conference.locationSantiago de Chile, CHLen
kaust.authorAzad, Mohammaden
kaust.authorMoshkov, Mikhailen
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