Active Feature Acquisition for Opinion Stream Classification under Drift

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung

Autoren

  • Ranjith Shivakumaraswamy
  • Christian Beyer
  • Vishnu Unnikrishnan
  • Eirini Ntoutsi
  • Myra Spiliopoulou

Organisationseinheiten

Externe Organisationen

  • Otto-von-Guericke-Universität Magdeburg
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des SammelwerksInteractive Adaptive Learning
UntertitelProceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019)
Seiten108-111
Seitenumfang4
PublikationsstatusVeröffentlicht - 2019
Veranstaltung2019 Workshop on Interactive Adaptive Learning, IAL 2019 - Wurzburg, Deutschland
Dauer: 16 Sept. 2019 → …

Publikationsreihe

NameCEUR Workshop Proceedings
Herausgeber (Verlag)CEUR Workshop Proceedings
Band2444
ISSN (Print)1613-0073

Abstract

Active stream learning is frequently used to acquire labels for instances and less frequently to determine which features should be considered as the stream evolves. We introduce a framework for active feature selection, intended to adapt the feature space of a polarity learner over a stream of opinionated documents. We report on the first results of our framework on substreams of reviews on different product categories.

ASJC Scopus Sachgebiete

Zitieren

Active Feature Acquisition for Opinion Stream Classification under Drift. / Shivakumaraswamy, Ranjith; Beyer, Christian; Unnikrishnan, Vishnu et al.
Interactive Adaptive Learning: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019). 2019. S. 108-111 (CEUR Workshop Proceedings; Band 2444).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung

Shivakumaraswamy, R, Beyer, C, Unnikrishnan, V, Ntoutsi, E & Spiliopoulou, M 2019, Active Feature Acquisition for Opinion Stream Classification under Drift. in Interactive Adaptive Learning: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019). CEUR Workshop Proceedings, Bd. 2444, S. 108-111, 2019 Workshop on Interactive Adaptive Learning, IAL 2019, Wurzburg, Deutschland, 16 Sept. 2019. <https://ceur-ws.org/Vol-2444/ialatecml_shortpaper3.pdf>
Shivakumaraswamy, R., Beyer, C., Unnikrishnan, V., Ntoutsi, E., & Spiliopoulou, M. (2019). Active Feature Acquisition for Opinion Stream Classification under Drift. In Interactive Adaptive Learning: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019) (S. 108-111). (CEUR Workshop Proceedings; Band 2444). https://ceur-ws.org/Vol-2444/ialatecml_shortpaper3.pdf
Shivakumaraswamy R, Beyer C, Unnikrishnan V, Ntoutsi E, Spiliopoulou M. Active Feature Acquisition for Opinion Stream Classification under Drift. in Interactive Adaptive Learning: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019). 2019. S. 108-111. (CEUR Workshop Proceedings).
Shivakumaraswamy, Ranjith ; Beyer, Christian ; Unnikrishnan, Vishnu et al. / Active Feature Acquisition for Opinion Stream Classification under Drift. Interactive Adaptive Learning: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019). 2019. S. 108-111 (CEUR Workshop Proceedings).
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title = "Active Feature Acquisition for Opinion Stream Classification under Drift",
abstract = "Active stream learning is frequently used to acquire labels for instances and less frequently to determine which features should be considered as the stream evolves. We introduce a framework for active feature selection, intended to adapt the feature space of a polarity learner over a stream of opinionated documents. We report on the first results of our framework on substreams of reviews on different product categories.",
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