Adaptive random forests for evolving data stream classification
Author:
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
http://link.springer.com/article/10.1007/s10994-017-5642-8/fulltext.html
Reference43 articles.
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2. Abdulsalam, H., Skillicorn, D. B, & Martin, P. (2008). Classifying evolving data streams using dynamic streaming random forests. In Database and expert systems applications (pp. 643–651). Springer.
3. Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S. (2003). A framework for clustering evolving data streams. In Proceedings of the 29th international conference on very large data bases, VLDB ’03 (Vol. 29, pp. 81–92). VLDB Endowment.
4. Agrawal, R., Imilielinski, T., & Swani, A. (1993). Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5(6), 914–925.
5. Amini, A., & Wah, T. Y. (2014). On density-based data streams clustering algorithms: A survey. Journal of Computer Science and Technology, 29(1), 116–141.
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