Relationships for Cost and Uncertainty of Decision Trees

Handle URI:
http://hdl.handle.net/10754/562534
Title:
Relationships for Cost and Uncertainty of Decision Trees
Authors:
Chikalov, Igor; Hussain, Shahid ( 0000-0002-1698-2809 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
This chapter is devoted to the design of new tools for the study of decision trees. These tools are based on dynamic programming approach and need the consideration of subtables of the initial decision table. So this approach is applicable only to relatively small decision tables. The considered tools allow us to compute: 1. Theminimum cost of an approximate decision tree for a given uncertainty value and a cost function. 2. The minimum number of nodes in an exact decision tree whose depth is at most a given value. For the first tool we considered various cost functions such as: depth and average depth of a decision tree and number of nodes (and number of terminal and nonterminal nodes) of a decision tree. The uncertainty of a decision table is equal to the number of unordered pairs of rows with different decisions. The uncertainty of approximate decision tree is equal to the maximum uncertainty of a subtable corresponding to a terminal node of the tree. In addition to the algorithms for such tools we also present experimental results applied to various datasets acquired from UCI ML Repository [4]. © Springer-Verlag Berlin Heidelberg 2013.
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; Computer Science Program
Journal:
Intelligent Systems Reference Library
Issue Date:
2013
DOI:
10.1007/978-3-642-30341-8_11
Type:
Article
ISSN:
18684394
ISBN:
9783642303401
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorChikalov, Igoren
dc.contributor.authorHussain, Shahiden
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2015-08-03T10:41:42Zen
dc.date.available2015-08-03T10:41:42Zen
dc.date.issued2013en
dc.identifier.isbn9783642303401en
dc.identifier.issn18684394en
dc.identifier.doi10.1007/978-3-642-30341-8_11en
dc.identifier.urihttp://hdl.handle.net/10754/562534en
dc.description.abstractThis chapter is devoted to the design of new tools for the study of decision trees. These tools are based on dynamic programming approach and need the consideration of subtables of the initial decision table. So this approach is applicable only to relatively small decision tables. The considered tools allow us to compute: 1. Theminimum cost of an approximate decision tree for a given uncertainty value and a cost function. 2. The minimum number of nodes in an exact decision tree whose depth is at most a given value. For the first tool we considered various cost functions such as: depth and average depth of a decision tree and number of nodes (and number of terminal and nonterminal nodes) of a decision tree. The uncertainty of a decision table is equal to the number of unordered pairs of rows with different decisions. The uncertainty of approximate decision tree is equal to the maximum uncertainty of a subtable corresponding to a terminal node of the tree. In addition to the algorithms for such tools we also present experimental results applied to various datasets acquired from UCI ML Repository [4]. © Springer-Verlag Berlin Heidelberg 2013.en
dc.subjectcost functionsen
dc.subjectDecision treeen
dc.subjectdynamic programmingen
dc.subjectuncertainty measuresen
dc.titleRelationships for Cost and Uncertainty of Decision Treesen
dc.typeArticleen
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.contributor.departmentComputer Science Programen
dc.identifier.journalIntelligent Systems Reference Libraryen
kaust.authorChikalov, Igoren
kaust.authorHussain, Shahiden
kaust.authorMoshkov, Mikhailen
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