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Keeping track of changing interests is a natural phenomenon as well as an interesting tracking problem because interests can emerge and diminish at different time frames. Being able to do so with a few feedback examples poses an even more important and challenging problem because existing concept drift learning algorithms that handle the task typically suffer from it. This paper presents a new computational Framework for Extending Incomplete Labeled Data Stream (FEILDS), which extends the capability of existing algorithms for learning concept drift from a few labeled data. The system transforms the original input stream into a new stream that can be conveniently tracked by the existing learning algorithms. The experiment results reveal that FEILDS can significantly improve the performances of a Multiple Three-Descriptor Representation (MTDR) algorithm, Rocchio algorithm, and window-based concept drift learning algorithms when learning from a sparsely labeled data stream with respect to their performances without using FEILDS.
Concept learning, relevance feedback, information filtering.

D. H. Widyantoro and J. Yen, "Relevant Data Expansion for Learning Concept Drift from Sparsely Labeled Data," in IEEE Transactions on Knowledge & Data Engineering, vol. 17, no. , pp. 401-412, 2005.
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