Journal
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
Volume 26, Issue 5, Pages 839-853Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S021848851850037X
Keywords
data streams; neural network; skewed distribution; ensemble learning; concept drift
Categories
Funding
- National Natural Science Foundation of China [61373127, 61772252]
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Data stream learning in non-stationary environments and skewed class distributions has been receiving more attention in machine learning communities. This paper proposes a novel ensemble classification method (ECSDS) for classifying data streams with skewed class distributions. In the proposed ensemble method, back-propagation neural network is selected as the base classifier. In order to demonstrate the effectiveness of our proposed method, we choose three baseline methods based on ECSDS and evaluate their overall performance on ten datasets from UCI machine learning repository. Moreover, the performance of incremental learning is also evaluated by these datasets. The experimental results show our proposed method can effectively deal with classification problems on non-stationary data streams with class imbalance.
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