Relevant data expansion for learning concept drift from sparsely labeled data

Dwi H. Widyantoro, John Yen

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)401-412
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number3
StatePublished - Mar 2005

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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