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Original version
Shivakumaraswamy, R.; Beyer, C.; Unnikrishnan, V.; Ntoutsi, E. ; Spiliopoulou, M.: Active feature acquisition for opinion stream classification under drift. In: Kottke, D.; Krempl, G.; Lemaire, V.; Holzinger, A.; Calma, A. (Eds.): Workshop & Tutorial on Interactive Adaptive Learning : IAL@ECML PKDD 2019 : the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019). Aachen, Germany : RWTH Aachen, 2019 (CEUR workshop proceedings ; 2444), S. 108-111.
Repository version
To cite the version in the repository, please use this identifier:
https://doi.org/10.15488/9818
Files in this item
| Abstract: | |
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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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| License of this version: | CC BY 4.0 Unported - https://creativecommons.org/licenses/by/4.0/ |
| Publication type: | BookPart |
| Publishing status: | publishedVersion |
| Publication date: | 2019 |
| Keywords german: | Konferenzschrift |
| Keywords english: | Active Feature Acquisition, Opinion Stream Classification, Feature acquisition, Feature space, Product categories, Stream classification, Sub-streams, Classification (of information) |
| DDC: | 004 | Informatik |
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Fakultät für Elektrotechnik und Informatik
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