In a standard classification framework, a discriminating rule is usually built from a trustworthy set of labeled units. In this context, test observations will be automatically classified as to have arisen from one of the known groups encountered in the training set, without the possibility of detecting previously unseen classes. To overcome this limitation, an adaptive semi-parametric Bayesian classifier is introduced for modeling the test units, where robust knowledge is extracted from the training set and incorporated within the priors’ model specification. A successful application of the proposed approach in a real-world problem is addressed.

Denti, F., Cappozzo, A., Greselin, F. (2020). Bayesian nonparametric adaptive classification with robust prior information. In A. Pollice, N. Salvati, F. Schirripa Spagnolo (a cura di), Book of Short Papers SIS 2020 (pp. 655-660). Pearson.

Bayesian nonparametric adaptive classification with robust prior information

Denti, F
Primo
;
Cappozzo, A
Secondo
;
Greselin F
Ultimo
2020

Abstract

In a standard classification framework, a discriminating rule is usually built from a trustworthy set of labeled units. In this context, test observations will be automatically classified as to have arisen from one of the known groups encountered in the training set, without the possibility of detecting previously unseen classes. To overcome this limitation, an adaptive semi-parametric Bayesian classifier is introduced for modeling the test units, where robust knowledge is extracted from the training set and incorporated within the priors’ model specification. A successful application of the proposed approach in a real-world problem is addressed.
Capitolo o saggio
Supervised classification, Unobserved classes, Bayesian adaptive learning, Bayesian mixture model, Stick-breaking prior
English
Book of Short Papers SIS 2020
9788891910776
Denti, F., Cappozzo, A., Greselin, F. (2020). Bayesian nonparametric adaptive classification with robust prior information. In A. Pollice, N. Salvati, F. Schirripa Spagnolo (a cura di), Book of Short Papers SIS 2020 (pp. 655-660). Pearson.
Denti, F; Cappozzo, A; Greselin, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/290334
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