Relationships among various parameters for decision tree optimization

Handle URI:
http://hdl.handle.net/10754/563340
Title:
Relationships among various parameters for decision tree optimization
Authors:
Hussain, Shahid ( 0000-0002-1698-2809 )
Abstract:
In this chapter, we study, in detail, the relationships between various pairs of cost functions and between uncertainty measure and cost functions, for decision tree optimization. We provide new tools (algorithms) to compute relationship functions, as well as provide experimental results on decision tables acquired from UCI ML Repository. The algorithms presented in this paper have already been implemented and are now a part of Dagger, which is a software system for construction/optimization of decision trees and decision rules. The main results presented in this chapter deal with two types of algorithms for computing relationships; first, we discuss the case where we construct approximate decision trees and are interested in relationships between certain cost function, such as depth or number of nodes of a decision trees, and an uncertainty measure, such as misclassification error (accuracy) of decision tree. Secondly, relationships between two different cost functions are discussed, for example, the number of misclassification of a decision tree versus number of nodes in a decision trees. The results of experiments, presented in the chapter, provide further insight. © 2014 Springer International Publishing Switzerland.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Springer Nature
Journal:
Innovations in Intelligent Machines-4
Issue Date:
14-Jan-2014
DOI:
10.1007/978-3-319-01866-9_13
Type:
Article
ISSN:
1860949X
ISBN:
9783319018652
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHussain, Shahiden
dc.date.accessioned2015-08-03T11:46:09Zen
dc.date.available2015-08-03T11:46:09Zen
dc.date.issued2014-01-14en
dc.identifier.isbn9783319018652en
dc.identifier.issn1860949Xen
dc.identifier.doi10.1007/978-3-319-01866-9_13en
dc.identifier.urihttp://hdl.handle.net/10754/563340en
dc.description.abstractIn this chapter, we study, in detail, the relationships between various pairs of cost functions and between uncertainty measure and cost functions, for decision tree optimization. We provide new tools (algorithms) to compute relationship functions, as well as provide experimental results on decision tables acquired from UCI ML Repository. The algorithms presented in this paper have already been implemented and are now a part of Dagger, which is a software system for construction/optimization of decision trees and decision rules. The main results presented in this chapter deal with two types of algorithms for computing relationships; first, we discuss the case where we construct approximate decision trees and are interested in relationships between certain cost function, such as depth or number of nodes of a decision trees, and an uncertainty measure, such as misclassification error (accuracy) of decision tree. Secondly, relationships between two different cost functions are discussed, for example, the number of misclassification of a decision tree versus number of nodes in a decision trees. The results of experiments, presented in the chapter, provide further insight. © 2014 Springer International Publishing Switzerland.en
dc.publisherSpringer Natureen
dc.titleRelationships among various parameters for decision tree optimizationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalInnovations in Intelligent Machines-4en
kaust.authorHussain, Shahiden
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