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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27763
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| Title: | Machine Learning from Imbalanced Data Sets 101 |
| Authors: | Provost, Foster |
| Issue Date: | 17-Nov-2008 |
| Series/Report no.: | CeDER-PP-2000-02 |
| Abstract: | For research to progress most effectively, we first should establish
common ground regarding just what is the problem that imbalanced data
sets present to machine learning systems. Why and when should imbalanced
data sets be problematic? When is the problem simply an artifact of
easily rectified design choices? I will try to pick the low-hanging
fruit and share them with the rest of the workshop participants.
Specifically, I would like to discuss what the problem is not. I hope
this will lead to a profitable discussion of what the problem indeed is,
and how it might be addressed most effectively. |
| Description: | Invited paper for the AAAI'2000 Workshop on Imbalanced Data Sets. |
| URI: | http://hdl.handle.net/2451/27763 |
| Appears in Collections: | CeDER Published Papers
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