Three important issues are often encountered in Supervised and Semi-Supervised Classification: class memberships are unreliable for some training units (label noise), a proportion of observations might depart from the main structure of the data (outliers) and new groups in the test set may have not been encountered earlier in the learning phase (unobserved classes). The present work introduces a robust and adaptive Discriminant Analysis rule, capable of handling situations in which one or more of the aforementioned problems occur. Two EM-based classifiers are proposed: the first one that jointly exploits the training and test sets (transductive approach), and the second one that expands the parameter estimation using the test set, to complete the group structure learned from the training set (inductive approach). Experiments on synthetic and real data, artificially adulterated, are provided to underline the benefits of the proposed method.

Cappozzo, A., Greselin, F., & Murphy, T. (2020). Anomaly and Novelty detection for robust semi-supervised learning. STATISTICS AND COMPUTING, 30(5), 1545-1571 [10.1007/s11222-020-09959-1].

Anomaly and Novelty detection for robust semi-supervised learning

Cappozzo, A
Primo
;
Greselin, F;
2020

Abstract

Three important issues are often encountered in Supervised and Semi-Supervised Classification: class memberships are unreliable for some training units (label noise), a proportion of observations might depart from the main structure of the data (outliers) and new groups in the test set may have not been encountered earlier in the learning phase (unobserved classes). The present work introduces a robust and adaptive Discriminant Analysis rule, capable of handling situations in which one or more of the aforementioned problems occur. Two EM-based classifiers are proposed: the first one that jointly exploits the training and test sets (transductive approach), and the second one that expands the parameter estimation using the test set, to complete the group structure learned from the training set (inductive approach). Experiments on synthetic and real data, artificially adulterated, are provided to underline the benefits of the proposed method.
Articolo in rivista - Articolo scientifico
Impartial Trimming ; Inductive Inference ; Label Noise ; Model - Based Classification ; Novelty Detection ; Outliers Detection ; Robust Estimation ; Transductive Inference ; Unobserved Classes
English
1545
1571
27
Cappozzo, A., Greselin, F., & Murphy, T. (2020). Anomaly and Novelty detection for robust semi-supervised learning. STATISTICS AND COMPUTING, 30(5), 1545-1571 [10.1007/s11222-020-09959-1].
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/10281/276200
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