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I want to efficiently label areas where the model is uncertain in its judgments within a specific cultural context to improve accuracy. Which data collection strategy is most efficient?
①Large-scale labeling using random sampling
②Cluster and label only representative samples.
③Reuse external data as-is.
④Rule-based automatic labeling
⑤Selecting uncertain samples in active learning
