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
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Bhattacharya, Sinchan, "NEAREST NEIGHBOR CLASSIFIERS WITH IMPROVED ACCURACY AND EFFICIENCY" (2017). Electrical Engineering Theses. 334.
https://mavmatrix.uta.edu/electricaleng_theses/334
Comments
Degree granted by The University of Texas at Arlington