ORCID Identifier(s)

0000-0002-6411-1207

Graduation Semester and Year

2017

Language

English

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Electrical Engineering

First Advisor

Michael T Manry

Second Advisor

Stephen R. Gibbs

Abstract

Nearest Neighbor algorithms are non-parametric algorithms that use distance measure techniques for classification and regressions. This thesis uses the method of pruning to improve accuracy and efficiency of a nearest neighbor classifier and also states the different stages the pruning algorithm can be applied and shows the best stage for pruning which gives the maximum accuracy. The performance of the classifier is shown to be better than other improved nearest neighbor classifiers. A fast method of finding the optimal k in a k-nearest neighbor classifier is proposed in the thesis. A method of optimizing the distance measure using a second order training algorithm in a k-nearest neighbor algorithm is also proposed in this thesis which results to better accuracy than the traditional k-nearest neighbor classifier.

Keywords

Nearest neighbor classifier, Classifiers, Pruning, Efficiency of classifier

Disciplines

Electrical and Computer Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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