In semi-supervised classification, class memberships are learnt from a trustworthy set of units. Despite careful data collection, some labels in the learning set could be unreliable (label noise). Further, a proportion of observations might depart from the main structure of the data (outliers) and new groups may appear in the test set, which were not encountered earlier in the training phase (unobserved classes). Therefore, we present here a robust and adaptive version of the Discriminant Analysis rule, capable of handling situations in which one or more of the aforementioned problems occur. The proposed approach is successfully employed in performing anomaly and novelty detection on geometric features recorded from X-ray photograms of grain kernels from different varieties.
Robust Model-Based Learning to Discover New Wheat Varieties and Discriminate Adulterated Kernels in X-Ray Images / A. Cappozzo, F. Greselin, T. Brendan Murphy (STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION). - In: Statistical Learning and Modeling in Data Analysis / [a cura di] S. Balzano, G.C. Porzio, R. Salvatore, D. Vistocco, M. Vichi. - [s.l] : Springer, 2021. - ISBN 9783030699437. - pp. 29-36 (( Intervento presentato al 12. convegno Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society tenutosi a Cassino nel 2019 [10.1007/978-3-030-69944-4_4].
Robust Model-Based Learning to Discover New Wheat Varieties and Discriminate Adulterated Kernels in X-Ray Images
A. Cappozzo;
2021
Abstract
In semi-supervised classification, class memberships are learnt from a trustworthy set of units. Despite careful data collection, some labels in the learning set could be unreliable (label noise). Further, a proportion of observations might depart from the main structure of the data (outliers) and new groups may appear in the test set, which were not encountered earlier in the training phase (unobserved classes). Therefore, we present here a robust and adaptive version of the Discriminant Analysis rule, capable of handling situations in which one or more of the aforementioned problems occur. The proposed approach is successfully employed in performing anomaly and novelty detection on geometric features recorded from X-ray photograms of grain kernels from different varieties.File | Dimensione | Formato | |
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