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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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