The 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, Alfredo; DI CARLO, Stefano; Politano, GIANFRANCO MICHELE MARIA. - 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].
Gene expression classifiers and out-of-class samples detection
BENSO, Alfredo;DI CARLO, STEFANO;POLITANO, GIANFRANCO MICHELE MARIA
2009
Abstract
The 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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2284755
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