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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 Exposure to UV Radiation
Author(s) -
HASHEMI RAY R.,
BAHAR MAHMOOD,
TANG NAN,
TYLER ALEXANDER A.,
HINSON WILLIAM
Publication year - 2003
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/j.1749-6632.2003.tb07523.x
Subject(s) - reduct , rough set , statistics , variable (mathematics) , mathematics , variables , confidence interval , classifier (uml) , discretization , algorithm , data mining , computer science , pattern recognition (psychology) , artificial intelligence , mathematical analysis
A bstract : 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.