HUMAN ANNOTATOR FOR IMBALANCED DATA
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 11, Pages 2327-2332
AbstractA difference in data collection is particularly important in the context of supervised machine learning involving two or more classes. Imbalanced means the number of available data points for different groups varies. Imbalanced sets are a special case for problem categorization where ad-measurement classes are not compatible between classes. Unbalanced groups are a common issue in classifying machine learning where a class has a disproportionate observer ratio. In several different fields, Imbalanced groups can be identified, including medical diagnosis, spam filtering, and fraud detection. Usually they consist of two classes, the class of majority and the class of minorities.
These types of data sets are usually found on websites that gather and compile data sets. These aggregators tend to provide data sets with several sources, without much remedy. That’s a good thing in this case-too much curation makes us too tidy data sets that are difficult to mark. Active learning is no doubt successful, but several recent studies have shown that active learning declines when applied on the outcomes. Human Annotator will gather data from the target in our project. See post information about laboratory experiments and different data sets, labelled and unlabelled. Users need to register their information to see their learning materials.
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