  Labeling mistakes are frequently encountered in real-world applications. If
not treated well, the labeling mistakes can deteriorate the classification
performances of a model seriously. To address this issue, we propose an
improved Naive Bayes method for text classification. It is analytically simple
and free of subjective judgements on the correct and incorrect labels. By
specifying the generating mechanism of incorrect labels, we optimize the
corresponding log-likelihood function iteratively by using an EM algorithm. Our
simulation and experiment results show that the improved Naive Bayes method
greatly improves the performances of the Naive Bayes method with mislabeled
data.
