Automatic design of a decision tree classifier employing neural networks

Persistent Link:
http://hdl.handle.net/10150/277881
Title:
Automatic design of a decision tree classifier employing neural networks
Author:
Rosten, David Paul, 1967-
Issue Date:
1991
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Pattern recognition problems involve two main issues: feature formulation and classifier design. This thesis is concerned with the latter. Numerous algorithms for the design of pattern recognition systems have been published, and the algorithm detailed herein is a new approach--specific to the design of decision tree classifiers. It involves a top-down strategy, optimizing the root node decision and then subsequently its children. To assess various pattern space partitions, the Tie statistical distance measure quantified the separability of potential cluster groupings. Additionally, a separate neural network was employed at each of the tree decision nodes. Results from the application of this methodology to the regional labeling of panchromatic images suggest it is a suitable approach.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Physics, Optics.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College
Degree Grantor:
University of Arizona
Advisor:
Hunt, Bobby R.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAutomatic design of a decision tree classifier employing neural networksen_US
dc.creatorRosten, David Paul, 1967-en_US
dc.contributor.authorRosten, David Paul, 1967-en_US
dc.date.issued1991en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractPattern recognition problems involve two main issues: feature formulation and classifier design. This thesis is concerned with the latter. Numerous algorithms for the design of pattern recognition systems have been published, and the algorithm detailed herein is a new approach--specific to the design of decision tree classifiers. It involves a top-down strategy, optimizing the root node decision and then subsequently its children. To assess various pattern space partitions, the Tie statistical distance measure quantified the separability of potential cluster groupings. Additionally, a separate neural network was employed at each of the tree decision nodes. Results from the application of this methodology to the regional labeling of panchromatic images suggest it is a suitable approach.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectPhysics, Optics.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorHunt, Bobby R.en_US
dc.identifier.proquest1343826en_US
dc.identifier.bibrecord.b26882577en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.