| Abstract: | Schemes that use both the imperfectly labeled and unlabeled pattern sets for the estimation of probabilities of label imperfections and correction of mislabels are presented in this paper. Using relationships between the class conditional densities, and a priori probabilities with and without imperfections in the labels, the problem of estimating probabilities of label imperfections is formulated as that of minimizing the Bayes probability of error. Experimental results are presented from the processing of remotely sensed multi-spectral scanner imagery data. A thresholding scheme is proposed for the correction of pattern mislabels. For a symmetric mislabeling case, a relationship is developed between the probability that such a scheme gives a bad label to a pattern and the probability that the scheme accepts the original label of the pattern. This relationship could be used for computing the threshold from unlabeled samples for a specified probability of bad labeling. An example is presented to illustrate the behavior of the scheme. Furthermore, bounds are presented between the Bayes errors with and without imperfections in the labels and are shown to become equalities when the imperfections in the labels become symmetric. |
