One-class learning and concept summarization for data streams

Publication Type:
Journal Article
Knowledge And Information Systems, 2011, 28 (3), pp. 523 - 553
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010005237OK.pdf1.11 MB
Adobe PDF
In this paper, we formulate a new research problem of concept learning and summarization for one-class data streams. The main objectives are to (1) allow users to label instance groups, instead of single instances, as positive samples for learning, and (2) summarize concepts labeled by users over the whole stream. The employment of the batch-labeling raises serious issues for stream-oriented concept learning and summarization, because a labeled instance group may contain non-positive samples and users may change their labeling interests at any time. As a result, so the positive samples labeled by users, over the whole stream, may be inconsistent and contain multiple concepts. To resolve these issues, we propose a one-class learning and summarization (OCLS) framework with two major components.
Please use this identifier to cite or link to this item: