A generative semi-supervised classifier for datasets with unknown classes

Stefan Schrunner, Bernhard Geiger, Anja Zernig, Roman Kern

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband


Classification has been tackled by a large number of algorithms, predominantly following a supervised learning setting. Surprisingly little research has been devoted to the problem setting where a dataset is only partially labeled, including even instances of entirely unlabeled classes. Algorithmic solutions that are suited for such problems are especially important in practical scenarios, where the labelling of data is prohibitively expensive, or the understanding of the data is lacking, including cases, where only a subset of the classes is known. We present a generative method to address the problem of semi-supervised classification with unknown classes, whereby we follow a Bayesian perspective. In detail, we apply a two-step procedure based on Bayesian classifiers and exploit information from both a small set of labeled data in combination with a larger set of unlabeled training data, allowing that the labeled dataset does not contain samples from all present classes. This represents a common practical application setup, where the labeled training set is not exhaustive. We show in a series of experiments that our approach outperforms state-of-the-art methods tackling similar semi-supervised learning problems. Since our approach yields a generative model, which aids the understanding of the data, it is particularly suited for practical applications.

TitelProceedings of the ACM Symposium on Applied Computing
Untertitel35th Annual ACM Symposium on Applied Computing, SAC 2020
Herausgeber (Verlag)Association of Computing Machinery
ISBN (elektronisch)9781450368667
PublikationsstatusVeröffentlicht - 30 Mär 2020
VeranstaltungThe 35th ACM/SIGAPP Symposium On Applied Computing: SAC2020 - Virtuell, Tschechische Republik
Dauer: 30 Mär 20203 Apr 2020


KonferenzThe 35th ACM/SIGAPP Symposium On Applied Computing
KurztitelSAC 2020
LandTschechische Republik

ASJC Scopus subject areas

  • Software


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