Prediction of Experimental Data for an Independent Variable Using the Experimental Data Collected for Other Independent Variables in a Study of Skin Cancer Caused by UV Radiation
Document Type
Article
Publication Date
7-7-2009
Publication Title
Annals of the New York Academy of Sciences
ISSN
1749-6632
Abstract
In this study, two algorithms (ONE and TWO) are introduced to determine the position of the t-distribution of variable V(i) (with 95% confidence) in the treated group in reference to the t-distribution of variable V(i) (with 95% confidence) in the control group of an experimental study involving UV radiation exposure of a group of rodents. The outcome of applying the two algorithms is two discretized files. A reduct of each file is generated using the rough sets methodology and then the measurements for one independent variable are predicted using the measurements of the other independent variables in the same reduct. The rough sets methodology and the fuzzy-rough classifier are used for this prediction. The results reveal that (1) algorithm TWO is the best, (2) the values for non-core variables are predicted with minimum accuracy of 87%, and (3) the prediction of values for core variables is not successful.
Recommended Citation
Hashemi, Ray R., Mahmood Bahar, Nan Tang, Alexander A. Tyler, William Hinson.
2009.
"Prediction of Experimental Data for an Independent Variable Using the Experimental Data Collected for Other Independent Variables in a Study of Skin Cancer Caused by UV Radiation."
Annals of the New York Academy of Sciences, 993 (1): 146-157: The New York Academy of Sciences.
source: https://nyaspubs.onlinelibrary.wiley.com/doi/10.1111/j.1749-6632.2003.tb07523.x
https://digitalcommons.georgiasouthern.edu/compsci-facpubs/233
