(2017), the probability P (y j |x i ; θ) of classifying instance x i into the wrong class y j can be directly regarded as the mislabeling risk of that instance.
our instance-level estimator is: Then cost-sensitive regularizer for each training instance can be written as: Note that if the probability of making correct prediction (i.e., P (y achieves its minimum when the probabilities of mislabeling x i into all incorrect classes are equal.