http://www.cnr.it/ontology/cnr/individuo/prodotto/ID68512
Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications (Articolo in rivista)
- Type
- Label
- Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications (Articolo in rivista) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.4236/jsea.2010.31005 (literal)
- Alternative label
Little S.; Colantonio S.; Salvetti O.; Perner P. (2010)
Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications
in Journal of software engineering and applications; Scientific Research Publishing, Inc., Petaluma, CA (Stati Uniti d'America)
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Little S.; Colantonio S.; Salvetti O.; Perner P. (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.scirp.org/journal/PaperInformation.aspx?paperID=1244 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
- In: Journal of Software Engineering and Applications, vol. 3 (1) pp. 39 - 49. Knowledge Research Publishing Inc, 2010. (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Note
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Institute of Computer Vision and Applied Computer Sciences (IBaI), Leipzig - Germany (Little S.; Perner P.)
CNR-ISTI, Pisa (Colantonio S.; Salvetti O.) (literal)
- Titolo
- Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications (literal)
- 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. (literal)
- Editore
- Prodotto di
- Autore CNR
- Insieme di parole chiave
Incoming links:
- Autore CNR di
- Prodotto
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
- Editore di
- Insieme di parole chiave di