Towards Addressing Problem-Distribution Drift with Case Discovery

Author:

Leake David,Schack Brian

Publisher

Springer Nature Switzerland

Reference32 articles.

1. Alippi, C., Roveri, M.: Just-in-time adaptive classifiers-part I: detecting nonstationary changes. IEEE Trans. Neural Netw. 19(7), 1145–1153 (2008)

2. Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448. SIAM (2007)

3. Cunningham, P., Nowlan, N., Delany, S., Haahr, M.: A case-based approach to spam filtering that can track concept drift. Technicaal report TCD-CS-2003-16, Computer Science Department, Trinity College Dublin (2003)

4. Dasu, T., Krishnan, S., Venkatasubramanian, S., Yi, K.: An information-theoretic approach to detecting changes in multi-dimensional data streams. In: Proceedings of Symposium on the Interface of Statistics, Computing Science, and Applications (Interface) (2006)

5. Delany, S.J., Cunningham, P., Tsymbal, A., Coyle, L.: A case-based technique for tracking concept drift in spam filtering. In: Macintosh, A., Ellis, R., Allen, T. (eds.) SGAI 2004, pp. 3–16. Springer, London (2005). https://doi.org/10.1007/1-84628-103-2_1

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