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Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications

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DOI: 10.4236/jsea.2010.31005    5,498 Downloads   9,381 Views   Citations

ABSTRACT

Many medical diagnosis applications are characterized by datasets that contain under-represented classes due to the fact that the disease is much rarer than the normal case. In such a situation classifiers such as decision trees and Naïve Bayesian that generalize over the data are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far are more appropriate for such a task. We propose to calculate the contingency table and class specific evaluation measures despite the overall accuracy for evaluation purposes of classifiers for these specific data characteristics. We evaluate the different options of our case-based classifier and compare the perform-ance to decision trees and Naïve Bayesian. Finally, we give an outlook for further work.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

S. LITTLE, S. COLANTONIO, O. SALVETTI and P. PERNER, "Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications," Journal of Software Engineering and Applications, Vol. 3 No. 1, 2010, pp. 39-49. doi: 10.4236/jsea.2010.31005.

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