Active Learning Data Selection via Mislabeling Scores

The system performs preliminary actions by pre-calculating confidence scores and mislabeling scores for data samples before manual annotation is needed. The neural network generates confidence scores indicating uncertainty, while the mislabeling score calculation identifies potential errors in advance, allowing annotators to focus only on high-priority samples that need manual annotation.

The patent introduces intermediary scoring mechanisms (confidence scores and mislabeling scores) that mediate between the neural network's automated labeling and human annotation. These scores serve as intermediaries to filter and prioritize data samples, reducing the direct complexity of evaluating all samples manually while maintaining high annotation efficiency.

The system applies local quality by differentiating the treatment of different data samples based on their individual characteristics. Samples with low confidence scores and high mislabeling scores are identified as having higher quality annotation needs, while samples with high confidence scores are processed automatically. This localized approach ensures that human annotation time is concentrated only where it provides maximum accuracy improvement.

The patent changes the parameters for selecting data samples from uniform random selection to targeted selection based on confidence scores and mislabeling scores. By transforming the selection criteria into score-based parameters, the system efficiently identifies the subset of samples that will yield the greatest accuracy improvement per unit of annotation time invested.

The patent segments the data samples into different categories based on their confidence scores and mislabeling scores. High-confidence samples are automatically labeled by the neural network, while low-confidence and high-mislabeling-risk samples are flagged for manual annotation. This segmentation allows the system to maintain high productivity through automated labeling while preserving reliability by having humans verify only the problematic samples.

The system implements feedback mechanisms where the neural network's confidence scores and mislabeling score calculations continuously inform the annotation process. After manual annotation of flagged samples, the corrected labels feed back into the neural network for retraining, creating a feedback loop that improves both productivity (through automated processing) and reliability (through targeted human verification and model improvement).