A generative semi-supervised classifier for datasets with unknown classes

Stefan Schrunner, Bernhard Geiger, Anja Zernig, Roman Kern

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Applied Computing
Subtitle of host publication35th Annual ACM Symposium on Applied Computing, SAC 2020
PublisherAssociation of Computing Machinery
Pages1066-1074
Number of pages9
ISBN (Electronic)9781450368667
DOIs
Publication statusPublished - 30 Mar 2020
EventThe 35th ACM/SIGAPP Symposium On Applied Computing: SAC2020 - Virtuell, Czech Republic
Duration: 30 Mar 20203 Apr 2020
https://www.sigapp.org/sac/sac2020/

Conference

ConferenceThe 35th ACM/SIGAPP Symposium On Applied Computing
Abbreviated titleSAC 2020
CountryCzech Republic
CityVirtuell
Period30/03/203/04/20
Internet address

Keywords

  • Bayes classifier
  • Gaussian mixture model
  • S-EM algorithm
  • Semi-supervised classification
  • Semi-supervised learning
  • Unknown classes

ASJC Scopus subject areas

  • Software

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  • Cite this

    Schrunner, S., Geiger, B., Zernig, A., & Kern, R. (2020). A generative semi-supervised classifier for datasets with unknown classes. In Proceedings of the ACM Symposium on Applied Computing: 35th Annual ACM Symposium on Applied Computing, SAC 2020 (pp. 1066-1074). Association of Computing Machinery. https://doi.org/10.1145/3341105.3373890