Conformal predictors, currently applied to many problems in various fields determine precise levels of confidence in new predictions on the basis only of the information present in the past data, without making recourse to any assumptions except that the examples are generated independently from the same probability distribution. In this paper, the robustness of their results is assessed for the cases in which the data are affected by error bars. This is the situation typical of the physical sciences, whose data are often the results of complex measurement procedures, unavoidably affected by noise. Assuming the noise presents a normal distribution, the Geodesic Distance on Gaussian Manifolds provides a statistical principled and quite effective method to handle the uncertainty in the data. A series of numerical tests prove that adopting this metric in conformal predictors improves significantly their performance, compared to the Euclidean distance, even for relatively low levels of noise.

Murari, A., Talebzadeh, S., Vega, J., Peluso, E., Gelfusa, M., Lungaroni, M., et al. (2016). A metric to improve the robustness of conformal predictors in the presence of error bars. In Conformal and probabilistic prediction with applications: 5th International Symposium, COPA 2016, Madrid, Spain, April 20-22, 2016: proceedings (pp.105-115). Springer Verlag [10.1007/978-3-319-33395-3_8].

A metric to improve the robustness of conformal predictors in the presence of error bars

TALEBZADEH, SAEED;PELUSO, EMMANUELE;GELFUSA, MICHELA;LUNGARONI, MICHELE;GAUDIO, PASQUALINO
2016

Abstract

Conformal predictors, currently applied to many problems in various fields determine precise levels of confidence in new predictions on the basis only of the information present in the past data, without making recourse to any assumptions except that the examples are generated independently from the same probability distribution. In this paper, the robustness of their results is assessed for the cases in which the data are affected by error bars. This is the situation typical of the physical sciences, whose data are often the results of complex measurement procedures, unavoidably affected by noise. Assuming the noise presents a normal distribution, the Geodesic Distance on Gaussian Manifolds provides a statistical principled and quite effective method to handle the uncertainty in the data. A series of numerical tests prove that adopting this metric in conformal predictors improves significantly their performance, compared to the Euclidean distance, even for relatively low levels of noise.
5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016
esp
2016
5.
Rilevanza internazionale
contributo
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
Settore FIS/01 - Fisica Sperimentale
English
Conformal predictors; Error bars; Geodesic distance; Inference methods;
Intervento a convegno
Murari, A., Talebzadeh, S., Vega, J., Peluso, E., Gelfusa, M., Lungaroni, M., et al. (2016). A metric to improve the robustness of conformal predictors in the presence of error bars. In Conformal and probabilistic prediction with applications: 5th International Symposium, COPA 2016, Madrid, Spain, April 20-22, 2016: proceedings (pp.105-115). Springer Verlag [10.1007/978-3-319-33395-3_8].
Murari, A; Talebzadeh, S; Vega, J; Peluso, E; Gelfusa, M; Lungaroni, M; Gaudio, P
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/167227
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