Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang

Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negat…