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Exploring the causes of seafood fraud: A meta-analysis on mislabeling and price
Marine Policy, 2019Seafood mislabeling is receiving increased attention by civil society, and programs and policies to address it are being implemented widely. Yet, evidence for the causes of mislabeling are largely limited to anecdotes and untested hypotheses. Mislabeling
Gloria M Luque
exaly +2 more sources
DNA barcoding revealing mislabeling of seafood in European mass caterings
Food Control, 2018Seafood is amongst the most internationally traded food commodities worldwide and it is one of the food groups most likely to be subject to fraud. A number of studies have been conducted where samples from retail-, restaurant- and food service outlets ...
Miguel Angel Pardo +2 more
exaly +2 more sources
Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization
International Conference on Machine LearningConcept Bottleneck Models (CBMs) propose to enhance the trustworthiness of AI systems by constraining their decisions on a set of human-understandable concepts.
Emiliano Penaloza +3 more
semanticscholar +1 more source
Assessment of marine fish mislabeling in South Korea's markets by DNA barcoding
Food Control, 2019Seafood is critically important to the human's diet worldwide because of its common and rich nutrients. However, seafood mislabeling may cause harms to consumers in terms of economic loss and health.
Thinh Dinh +2 more
exaly +2 more sources
Identifying and Handling Mislabelled Instances
Journal of Intelligent Information Systems, 2004Data mining and knowledge discovery aim at producing useful and reliable models from the data. Unfortunately some databases contain noisy data which perturb the generalization of the models. An important source of noise consists of mislabelled training instances.
Fabrice Muhlenbach +2 more
openaire +1 more source
DNA barcoding revealing seafood mislabeling in food services from Spain
, 2020Commercialization of seafood is one of the most complex food chains at international level and it is therefore one of the food commodities most likely to face challenges related to fraud and authenticity.
M. Pardo, Elisa Jiménez
semanticscholar +1 more source
An algorithm for correcting mislabeled data
Intelligent Data Analysis, 2001Reliable evaluation for the performance of classifiers depends on the quality of the data sets on which they are tested. During the collecting and recording of a data set, however, some noise may be introduced into the data, especially in various real-world environments, which can degrade the quality of the data set.
Xinchuan Zeng, Tony R. Martinez
openaire +3 more sources
Shark Mislabeling Threatens Biodiversity
Science, 2013As commercial fisheries struggle to apply regulatory and legal mechanisms that depend on reliable species-specific data ([ 1 ][1]), the shark industry faces an even greater obstacle to transparency: Sellers change product names to overcome consumer resistance.
Hugo, Bornatowski +2 more
openaire +2 more sources
Fixing Mislabeling by Human Annotators Leveraging Conflict Resolution and Prior Knowledge
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, 2019According to the "human in the loop" paradigm, machine learning algorithms can improve when leveraging on human intelligence, usually in the form of labels or annotation from domain experts.
M. Zeni +4 more
semanticscholar +1 more source