Three important issues are often encountered in Supervised Classification: class-memberships are unreliable for some training units (Label Noise), a proportion of observations might depart from the bulk of the data structure (Outliers) and groups represented in the test set may have not been encountered earlier in the learning phase (Unobserved Classes). The present work introduces a Robust and AdaptiveEigenvalue-Decomposition Discriminant Analysis (RAEDDA) capable of handling situations in which one or more of the afore-described problems occur. Transductiveand inductive robust EM-based procedures are proposed for parameter estimation and experiments on real data, artificially adulterated, are provided to underline the benefits of the proposed method.

Cappozzo, A., Greselin, F., Murphy, T. (2019). Supervised learning in presence of outliers, label noise and unobserved classes. In G. Porzio, F. Greselin, S. Balzano (a cura di), Book of short papers | Cladag2019 (pp. 104-107). Centro Editoriale di Ateneo Università di Cassino e del Lazio Meridionale.

Supervised learning in presence of outliers, label noise and unobserved classes

Cappozzo, A
Primo
;
Greselin, F
Secondo
;
2019

Abstract

Three important issues are often encountered in Supervised Classification: class-memberships are unreliable for some training units (Label Noise), a proportion of observations might depart from the bulk of the data structure (Outliers) and groups represented in the test set may have not been encountered earlier in the learning phase (Unobserved Classes). The present work introduces a Robust and AdaptiveEigenvalue-Decomposition Discriminant Analysis (RAEDDA) capable of handling situations in which one or more of the afore-described problems occur. Transductiveand inductive robust EM-based procedures are proposed for parameter estimation and experiments on real data, artificially adulterated, are provided to underline the benefits of the proposed method.
Capitolo o saggio
model-based classification, unobserved classes, label noise, outliers detection, impartial trimming, robust estimation
English
Book of short papers | Cladag2019
978-88-8317-108-6
Cappozzo, A., Greselin, F., Murphy, T. (2019). Supervised learning in presence of outliers, label noise and unobserved classes. In G. Porzio, F. Greselin, S. Balzano (a cura di), Book of short papers | Cladag2019 (pp. 104-107). Centro Editoriale di Ateneo Università di Cassino e del Lazio Meridionale.
Cappozzo, A; Greselin, F; Murphy, T
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/257199
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