Optimization of the Data Labeling Process in Weakly Supervised Environments
The article investigates the problem of increasing the efficiency of the data labeling process in poorly controlled environments based on Active Learning methods. The relevance of the work is due to the rapid growth of unstructured and partially labeled data, the high cost of manual annotation, the shortage of qualified experts, and the negative impact of noise labels on the quality of machine learning models.