An Evaluation of Various On-line Classification Algorithms in Nonstationary Environments
On-line classification algorithms are useful in cases were data is streamed in large amounts, which is quite common in todays society. However, when the data starts to drift (i.e. concept drift) it might lead to lower prediction accuracy. For example seasonal changes, climate changes, weather sensor...
| Main Authors: | , |
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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2020
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| Subjects: | |
| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280406 |