I have a dataset with around 1% of mislabeled data, it is a multi label problem and i want to find a way to correct those incorrect labels.

Assuming that the amount of mislabeled data is low i divided the dataset in Train/Test and trained a classifier taking care that the classifier does not overfit.

After that i know that the accuracy on the Test set is as high as possible i evaluated the whole dataset using the classifier and the result is a new set of labels which i assume that are the corrected labels.

Is this the correct approach to solve a problem like this?