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P. Perner, U. Zscherpel, and C. Jacobsen, “A comparision between neural networks and decision trees based on data from industrial radiographic testing,” Pattern Recognition Letters, Vol. 22, pp. 47–54, 2001.

has been cited by the following article:

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

  AUTHORS: Suzanne LITTLE, Sara COLANTONIO, Ovidio SALVETTI, Petra PERNER

  KEYWORDS: Feature Subset Selection, Feature Weighting, Prototype Selection, Evaluation of Methods, Prototype-Based Classification, Methodology for Prototype-Based Classification, CBR in Health

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.3 No.1, January 28, 2010

  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.