Product mislabeling, adulteration, and substitution are increasing concerns in highly processed foods, including pet foods. Although regulations exist for pet foods, there is currently a lack of information on the prevalence of pet food mislabeling. The objective of this study was to perform a market survey of pet foods and pet treats marketed for domestic canines and felines to identify meat s...

The average probability of error is used to demonstrate performance of a Bayesian classification test (referred to as the Combined Bayes Test (CBT)) given the training data of each class are mislabeled. The CBT combines the information in discrete training and test data to infer symbol probabilities, where a uniform Dirichlet prior (i.e., a noninformative prior of complete ignorance) is assumed...

We discuss robustness against mislabeling in multiclass labels for classification problems and propose two algorithms of boosting, the normalized Eta-Boost.M and Eta-Boost.M, based on the Eta-divergence. Those two boosting algorithms are closely related to models of mislabeling in which the label is erroneously exchanged for others. For the two boosting algorithms, theoretical aspects supportin...

Practice changes cause the mislabeling of batched drugs.

CONTEXT Accurate specimen labeling is a major patient-safety initiative by the Joint Commission and the College of American Pathologists. Inadequate specimen labels have led to patient injury from wrong patient diagnosis, wrong side treatment, and delay in diagnosis. OBJECTIVES To quantify the rates of mislabeled cases, specimens, blocks, and slides and to identify the sources of error and th...

Increased education of consumers can be an effective tool for conservation of commercially harvested marine species when product labeling is accurate and allows an informed choice. However, generic labeling (e.g., as white fish or surimi) and mislabeling of seafood prevents this and may erode consumer confidence in seafood product labels in general. We used DNA barcoding to identify the species...

The self-training methods have been praised by extensive research in semi-supervised classification. Mislabeling is the main challenge methods. Multiple variations of are recently proposed against mislabeling from following one two aspects: a) using heuristic rules to find high-confidence unlabeled samples that can easily be predicted correctly each iteration; b) enhancing prediction performanc...