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Staging of prostate cancer using automatic feature selection, sampling and Dempster-Shafer fusion.

Chandana S, Leung H, Trpkov K - Cancer Inform (2009)

Bottom Line: 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).

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada.

ABSTRACT
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).

No MeSH data available.


Related in: MedlinePlus

ROC curves of KNN and PCA Features.
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Related In: Results  -  Collection

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f6-cin-07-57: ROC curves of KNN and PCA Features.


Staging of prostate cancer using automatic feature selection, sampling and Dempster-Shafer fusion.

Chandana S, Leung H, Trpkov K - Cancer Inform (2009)

ROC curves of KNN and PCA Features.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2664701&req=5

f6-cin-07-57: ROC curves of KNN and PCA Features.
Bottom Line: 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).

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada.

ABSTRACT
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).

No MeSH data available.


Related in: MedlinePlus