Sandeep Chandana1, Henry Leung1 and Kiril Trpkov2
1Department of Electrical and Computer Engineering, University of Calgary, ICT-402, 2500 University Drive NW, Calgary, Alberta, T2N 1N4 Canada. 2Department of Pathology and Laboratory Medicine, Calgary Laboratory Services, Calgary, Alberta T2V 1P9 Canada.
A novel technique of automatically selecting the best pairs of features and sampling techniques to predict the stage of prostate cancer is proposed in this study. The problem of class imbalance, which is prominent in most medical data sets is also addressed here. Three feature subsets obtained by the use of principal components analysis (PCA), genetic algorithm (GA) and rough sets (RS) based approaches were also used in the study. The performance of under-sampling, synthetic minority over-sampling technique (SMOTE) and a combination of the two were also investigated and the performance of the obtained models was compared. To combine the classifier outputs, we used the Dempster-Shafer (DS) theory, whereas the actual choice of combined models was made using a GA. We found that the best performance for the overall system resulted from the use of under sampled data combined with rough sets based features modeled as a support vector machine (SVM).
PDF (790.53 KB PDF FORMAT)
RIS citation (ENDNOTE, REFERENCE MANAGER, PROCITE, REFWORKS)
BibTex citation (BIBDESK, LATEX)
I would like to extend my gratitude for creating the next generation of a scientific journal -- the science journal of tomorrow. The entire process bespoke of exceptional efficiency, celerity, professionalism, competency, and service.
All authors are surveyed after their articles are published. Authors are asked to rate their experience in a variety of areas, and their responses help us to monitor our performance. Presented here are their responses in some key areas. No 'poor' or 'very poor' responses were received; these are represented in the 'other' category.See Our Results
Copyright © 2014 Libertas Academica Ltd (except open access articles and accompanying metadata and supplementary files.)