Robust Ensemble Learning For Mining Noisy Data Streams

Publisher:
Elsevier Science Bv
Publication Type:
Journal Article
Citation:
Decision Support Systems, 2011, 50 (2), pp. 469 - 479
Issue Date:
2011-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011000602OK.pdf777.01 kB
Adobe PDF
In this paper, we study the problem of learning from concept drifting data streams with noise, where samples in a data stream may be mislabeled or contain erroneous values. Our essential goal is to build a robust prediction model from noisy stream data t
Please use this identifier to cite or link to this item: