Date of Award
2015
Publisher
North Dakota State University
Document Type
Thesis
Degree Awarded
Master of Science (MS)
Program
Statistics
Department
Statistics
College
Science and Mathematics
Faculty Advisor
Yang, Yarong
Description
This paper compares four classifications tools, Support Vector Machine (SVM), Random Forest (RF), DualKS and the k-Nearest Neighbors (kNN) that are based on different statistical learning theories. The dataset used is a microarray gene expression of 596 male patients with prostate cancer. After treatment, the patients were classified into one group of phenotype with three levels: PSA (Prostate-Specific Antigen), Systematic and NED (No Evidence of Disease). The purpose of this research is to determine the performance rate of each classifier by selecting the optimal kernels and parameters that give the best prediction rate of the phenotype. The paper begins with the discussion of previous implementations of the tools and their mathematical theories. The results showed that three classifiers achieved a comparable performance that was above the average while DualKS did not. We also observed that SVM outperformed the kNN, RF and DualKS classifiers.
Recommended Citation
Sakouvogui, Kekoura, "Comparative Classification of Prostate Cancer Data using the Support Vector Machine, Random Forest, Dualks and k-Nearest Neighbours" (2015). Statistics. 112.
https://digitalcommons.ndsu.edu/stats/112
Rights
NDSU Policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/27698