Relevant data expansion for learning concept drift from sparsely labeled data
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