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EnglishThe proper application of statistics, machine learning, and data-mining techniques in routine clinical diagnostics to classify diseases using their genetic expression profile is still a challenge. One critical issue is the overall inability of most state-of-the-art classifiers to identify out-of-class samples, i.e., samples that do not belong to any of the available classes. This paper shows a possible explanation for this problem and suggests how, by analyzing the distribution of the class probability estimates generated by a classifier, it is possible to build decision rules able to significantly improve its performances.
Gene expression classifiers and out-of-class samples detection / Benso A.; Di Carlo S.; Politano G.. - STAMPA. - (2009), pp. 1-5. ((Intervento presentato al convegno IEEE 9th International Conference on Information Technology and Applications in Biomedicine (ITAB) tenutosi a Larnaca, CY nel 5-7 Nov. 2009 [10.1109/ITAB.2009.5394401].
| Titolo: | Gene expression classifiers and out-of-class samples detection | |
| Autori: | ||
| Data di pubblicazione: | 2009 | |
| Abstract: | The proper application of statistics, machine learning, and data-mining techniques in routine cli...nical diagnostics to classify diseases using their genetic expression profile is still a challenge. One critical issue is the overall inability of most state-of-the-art classifiers to identify out-of-class samples, i.e., samples that do not belong to any of the available classes. This paper shows a possible explanation for this problem and suggests how, by analyzing the distribution of the class probability estimates generated by a classifier, it is possible to build decision rules able to significantly improve its performances. | |
| ISBN: | 9781424453795 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 2009-ITAB-Rule.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2284755
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