Skip to Main Content
Frequently Asked Questions
Submit an ETD
Global Search Box
Need Help?
Keyword Search
Participating Institutions
Advanced Search
School Logo
Files
File List
19613.pdf (1.33 MB)
ETD Abstract Container
Abstract Header
Exploring the Noise Resilience of Combined Sturges Algorithm
Author Info
Agarwal, Akrita
ORCID® Identifier
http://orcid.org/0000-0001-7832-8362
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447070335
Abstract Details
Year and Degree
2015, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Abstract
Over the years, various Classification algorithms have been developed. Two of the most popular Classification algorithms are - Naive Bayes and κnn. They are both unique in their approaches towards classification. Naive Bayes uses the statistical component of the data, being the frequency of datapoints, while κnn leverages the geometrical aspect, usually the class membership of the κ nearest datapoints. In 2013, Ralescu developed the Combined Sturges algorithm, that uses both the geometrical and statistical components of the dataset. This study implements a noise model on synthetic and real world datasets to compare the noise resilience of the three algorithms. It is mainly an explorative study aimed at identifying the most robust algorithm.
Committee
Anca Ralescu, Ph.D. (Committee Chair)
Kenneth Berman, Ph.D. (Committee Member)
Dan Ralescu, Ph.D. (Committee Member)
Pages
78 p.
Subject Headings
Computer Science
Keywords
Noise Resilience
;
Machine Learning Algorithms
;
Combined Sturges
;
Naive Bayes
;
k nearest neighbor
Recommended Citations
Refworks
EndNote
RIS
Mendeley
Citations
Agarwal, A. (2015).
Exploring the Noise Resilience of Combined Sturges Algorithm
[Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447070335
APA Style (7th edition)
Agarwal, Akrita.
Exploring the Noise Resilience of Combined Sturges Algorithm.
2015. University of Cincinnati, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447070335.
MLA Style (8th edition)
Agarwal, Akrita. "Exploring the Noise Resilience of Combined Sturges Algorithm." Master's thesis, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447070335
Chicago Manual of Style (17th edition)
Abstract Footer
Document number:
ucin1447070335
Download Count:
142
Copyright Info
© 2015, some rights reserved.
Exploring the Noise Resilience of Combined Sturges Algorithm by Akrita Agarwal is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.