Abstract
In data streams new classes can appear over time due to changes in the data statistical distribution. Consequently, models can become outdated, which requires the use of incremental learning algorithms capable of detecting and learning the changes over time. However, when a single classification model is used for novelty detection, there is a risk that its bias may not be suitable for new data distributions. A solution could be the combination of several models into an ensemble. Besides, because models can only be updated when labeled data arrives, we propose two unsupervised ensemble approaches: one combining clustering partitions using the same clustering technique; and other using different clustering techniques. We compare the performance of the proposed methods with well known novelty detection algorithms. The methods were tested on datasets commonly used in the novelty detection literature. The experimental results show that proposed ensembles have competitive performance for novelty detection in data streams.
Original language | English |
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Title of host publication | Discovery Science |
Subtitle of host publication | 22nd International Conference, DS 2019, Splitm, Croatia, October 28-30, 2019. Proceedings |
Editors | Petra Kralj Novak, Sašo Džeroski, Tomislav Šmuc |
Place of Publication | Cham |
Publisher | Springer |
Pages | 460-470 |
Number of pages | 11 |
ISBN (Electronic) | 978-3-030-33778-0 |
ISBN (Print) | 978-3-030-33777-3 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Event | 22nd International Conference on Discovery Science, DS 2019 - Radisson Blu Resort and Spa, Split, Croatia Duration: 28 Oct 2019 → 30 Oct 2019 https://ds2019.irb.hr/ |
Publication series
Name | Lecture Notes in Artificial Intelligence; subseries of Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 11828 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 22nd International Conference on Discovery Science, DS 2019 |
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Abbreviated title | DS2019 |
Country/Territory | Croatia |
City | Split |
Period | 28/10/19 → 30/10/19 |
Internet address |
Keywords
- Clustering
- Data streams
- Ensembles
- Novelty detection