Transactions on Machine Learning and Data Mining (ISSN: 1865-6781)
Volume 2 - Number 1 - July 2009 - Page 44-61
Can Prototype-Based Classification be a good Method for Biomedical Applications?
S. Little 1, S. Colatino2, O. Salvetti2 and P. Perner1
1Institute of Computer Vision and Applied Computer Sciences IBaI, Leipzig, Germany
2ISTI-CNR, Pisa, Italy
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 performance to decision trees and Naïve Bayesian. Finally, we give an outlook for further work.
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