Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error

19 Sep 2018Nontawat CharoenphakdeeMasashi Sugiyama

Bottlenecks of binary classification from positive and unlabeled data (PU classification) are the requirements that given unlabeled patterns are drawn from the test marginal distribution, and the penalty of the false positive error is identical to the false negative error. However, such requirements are often not fulfilled in practice... (read more)

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