Reference20 articles.
1. Tsymbal, A.: The problem of concept drift: definitions and related work. Technical report TCD-CS-2004-15, Trinity College Dublin, Ireland, pp. 1–7 (2004)
2. Masud, M., Chen, Q., Khan, L., Aggarwal, C., Gao, J., Han, J., Thuraisingham, B.: Addressing concept-evolution in concept-drifting data streams. IEEE International Conference on Data Mining, pp. 929–934 (2010)
3. Alippi, C., Boracchi, G.,Roveri, M.: An effective just-in-time adaptive classifier for gradual concept drifts. In: International Joint Conference on Neural Networks, pp. 1675–1682 (2011)
4. Brzezinski, D., Stefanowski, J.: Accuracy updated ensemble for data streams with concept drift. In: 6th International Conference on Hybrid Artificial Intelligent Systems. Lecture Notes in Computer Science, vol. 6679, pp. 155–162. Springer (2011)
5. Shetty, S., Mukkavilli, S.K., Keel, L.H.: An integrated machine learning and control theoretic model for mining concept-drifting data streams. In: IEEE International Conference on Technologies for Homeland Security (HST), pp. 75–80 (2011)
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献