Using neural networks within the leaves of a classification tree

dc.contributor.advisorButtrey, Samuel E.
dc.contributor.authorChen, Chia-sheng
dc.dateJune, 2000
dc.date.accessioned2012-08-09T19:28:10Z
dc.date.available2012-08-09T19:28:10Z
dc.date.issued2000-06
dc.description.abstractClassification trees and neural networks are widely used individually, yet little is known about the effect of combining these two techniques. Earlier work has shown that using k-nearest neighbor (k-NN) inside the leaves of a tree can increase classification accuracy. Since neural networks are so powerful, we apply neural networks instead of the k-NN method inside the leaves of the tree. This thesis studies the performance of this composite classifier. It is compared to the tree-structured classifier and the neural network classifier. We use commonly available data sets in this application and compare the results to those generated by other generally used classifiers. Compared to the results of the other two classifiers in this thesis, the composite classifier always gives the lowest cross-validated misclassification error rates in these data sets. Its excellent performance tells us that it is worth further investigation.en_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.
dc.description.serviceRepublic of China (Taiwan) Army author.en_US
dc.description.urihttp://archive.org/details/usingneuralnetwo109459253
dc.format.extentxviii, 63 p.;28 cm.en_US
dc.identifier.urihttp://handle.dtic.mil/100.2/ADA380713
dc.identifier.urihttps://hdl.handle.net/10945/9253
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.titleUsing neural networks within the leaves of a classification treeen_US
dc.typeThesisen_US
dspace.entity.typePublication
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.nameM.S. in Operations Researchen_US
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