Three approaches to deal with inconsistent decision tables - Comparison of decision tree complexity

Handle URI:
http://hdl.handle.net/10754/564665
Title:
Three approaches to deal with inconsistent decision tables - Comparison of decision tree complexity
Authors:
Azad, Mohammad ( 0000-0001-9851-1420 ) ; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
In inconsistent decision tables, there are groups of rows with equal values of conditional attributes and different decisions (values of the decision attribute). We study three approaches to deal with such tables. Instead of a group of equal rows, we consider one row given by values of conditional attributes and we attach to this row: (i) the set of all decisions for rows from the group (many-valued decision approach); (ii) the most common decision for rows from the group (most common decision approach); and (iii) the unique code of the set of all decisions for rows from the group (generalized decision approach). We present experimental results and compare the depth, average depth and number of nodes of decision trees constructed by a greedy algorithm in the framework of each of the three approaches. © 2013 Springer-Verlag.
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:
Springer Science + Business Media
Journal:
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Conference/Event name:
14th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2013
Issue Date:
2013
DOI:
10.1007/978-3-642-41218-9_6
Type:
Conference Paper
ISSN:
03029743
ISBN:
9783642412172
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.authorChikalov, Igoren
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2015-08-04T07:11:30Zen
dc.date.available2015-08-04T07:11:30Zen
dc.date.issued2013en
dc.identifier.isbn9783642412172en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-642-41218-9_6en
dc.identifier.urihttp://hdl.handle.net/10754/564665en
dc.description.abstractIn inconsistent decision tables, there are groups of rows with equal values of conditional attributes and different decisions (values of the decision attribute). We study three approaches to deal with such tables. Instead of a group of equal rows, we consider one row given by values of conditional attributes and we attach to this row: (i) the set of all decisions for rows from the group (many-valued decision approach); (ii) the most common decision for rows from the group (most common decision approach); and (iii) the unique code of the set of all decisions for rows from the group (generalized decision approach). We present experimental results and compare the depth, average depth and number of nodes of decision trees constructed by a greedy algorithm in the framework of each of the three approaches. © 2013 Springer-Verlag.en
dc.publisherSpringer Science + Business Mediaen
dc.subjectBoundary Subtablesen
dc.subjectDecision Treesen
dc.subjectGreedy Algorithmsen
dc.subjectInconsistent Decision Tablesen
dc.titleThree approaches to deal with inconsistent decision tables - Comparison of decision tree complexityen
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.journalRough Sets, Fuzzy Sets, Data Mining, and Granular Computingen
dc.conference.date11 October 2013 through 14 October 2013en
dc.conference.name14th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2013en
dc.conference.locationHalifax, NSen
kaust.authorAzad, Mohammaden
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
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