Document - Evaluation of feature subset selection, feature weighting, and prototype selection for biomedical applications

2008

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Evaluation of feature subset selection, feature weighting, and prototype selection for biomedical applications

Little S., Salvetti O., Perner P.

Feature Subset Selection Feature Weighting CBR in Health

Many medical diagnosis applications are characterized by datasets that contain under-represented classes due to the fact that the disease appears more rarely than the normal case. In such a situation classifiers that generalize over the data such as decision trees and Naïve Bayesian 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.

Source: ECCBR 2008 - Advances in Case-Based Reasoning, 9th European Conference, pp. 312–324, Trier, Germany, 1-4 September 2008

Publisher: Springer-Verlag - Berlin Heidelberg New York, Berlin, DEU

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:44167,
	title = {Evaluation of feature subset selection, feature weighting, and prototype selection for biomedical applications},
	author = {Little S. and Salvetti O. and Perner P.},
	publisher = {Springer-Verlag - Berlin Heidelberg New York, Berlin, DEU},
	doi = {10.1007/978-3-540-85502-6},
	booktitle = {ECCBR 2008 - Advances in Case-Based Reasoning, 9th European Conference, pp. 312–324, Trier, Germany, 1-4 September 2008},
	year = {2008}
}