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In addition to unbalanced classes, where let's say 2% of data is from a minority class and 98% is from the majority, how do people handle when 2% of data is fro
by fizx 10y ago
In addition to unbalanced classes, where let's say 2% of data is from a minority class and 98% is from the majority, how do people handle when 2% of data is from the minority and 98% is of an unknown class, but pulled from a known distribution?
- micro_cam 10y agohttps://en.wikipedia.org/wiki/Semi-supervised_learning https://en.wikipedia.org/wiki/Semi-supervised_learning
- ausvisaissues 10y agoI am not completely following what you mean by "98% is of an unknown class, but pulled from a known distribution". Are you suggesting that: 2 percent of samples are positive, drawn from p(x|y=1) 98 percent of samples are drawn from a distribution p(x), but may be either positive or negative? The setting that you described above is called "positive and unlabeled (PU)" learning. This paper: http://cseweb.ucsd.edu/~elkan/posonly.pdf http://cseweb.ucsd.edu/~elkan/posonly.pdf is one of the seminal articles on the topic (although the equation on the bottom of page 214 contains a statement that may not necessarily hold true). There are quite a lot more recent papers on this topic.
- deleted 10y ago[deleted]