Multi-pruning of decision trees for knowledge representation and classification

Handle URI:
http://hdl.handle.net/10754/621280
Title:
Multi-pruning of decision trees for knowledge representation and classification
Authors:
Azad, Mohammad ( 0000-0001-9851-1420 ) ; Chikalov, Igor; Hussain, Shahid ( 0000-0002-1698-2809 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
We consider two important questions related to decision trees: first how to construct a decision tree with reasonable number of nodes and reasonable number of misclassification, and second how to improve the prediction accuracy of decision trees when they are used as classifiers. We have created a dynamic programming based approach for bi-criteria optimization of decision trees relative to the number of nodes and the number of misclassification. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments on datasets from UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in number of misclassification. Based on the created approach we have proposed a multi-pruning procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. © 2015 IEEE.
KAUST Department:
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Citation:
Azad M, Chikalov I, Hussain S, Moshkov M (2015) Multi-pruning of decision trees for knowledge representation and classification. 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR). Available: http://dx.doi.org/10.1109/ACPR.2015.7486574.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)
Conference/Event name:
3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
Issue Date:
9-Jun-2016
DOI:
10.1109/ACPR.2015.7486574
Type:
Conference Paper
Appears in Collections:
Conference Papers

Full metadata record

DC FieldValue Language
dc.contributor.authorAzad, Mohammaden
dc.contributor.authorChikalov, Igoren
dc.contributor.authorHussain, Shahiden
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2016-11-03T06:56:44Z-
dc.date.available2016-11-03T06:56:44Z-
dc.date.issued2016-06-09en
dc.identifier.citationAzad M, Chikalov I, Hussain S, Moshkov M (2015) Multi-pruning of decision trees for knowledge representation and classification. 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR). Available: http://dx.doi.org/10.1109/ACPR.2015.7486574.en
dc.identifier.doi10.1109/ACPR.2015.7486574en
dc.identifier.urihttp://hdl.handle.net/10754/621280-
dc.description.abstractWe consider two important questions related to decision trees: first how to construct a decision tree with reasonable number of nodes and reasonable number of misclassification, and second how to improve the prediction accuracy of decision trees when they are used as classifiers. We have created a dynamic programming based approach for bi-criteria optimization of decision trees relative to the number of nodes and the number of misclassification. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments on datasets from UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in number of misclassification. Based on the created approach we have proposed a multi-pruning procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. © 2015 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleMulti-pruning of decision trees for knowledge representation and classificationen
dc.typeConference Paperen
dc.contributor.departmentKing Abdullah University of Science and Technology, Thuwal, Saudi Arabiaen
dc.identifier.journal2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)en
dc.conference.date3 November 2016 through 6 November 2016en
dc.conference.name3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015en
kaust.authorAzad, Mohammaden
kaust.authorChikalov, Igoren
kaust.authorHussain, Shahiden
kaust.authorMoshkov, Mikhailen
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