Machine Learning Model Label Accuracy via Confidence Threshold Filtering
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Solution Overview
Problem
Human errors in labeling training and testing data for machine-learning models lead to inaccuracies and reduced performance, as humans often miss or incorrectly apply labels, affecting model performance and evaluation metrics.
Innovation Solution
A computing system that uses a machine-learning model to identify potential labels within a document, assign confidence values, and filter them through a confidence threshold, presenting discrepancies to a reviewer for validation and updating the training data, allowing for targeted and simplified relabeling tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If humans manually label training data, then labeling can be done with current technology, but human errors lead to reduced model performance and inaccurate evaluations
Solution Approach 1:
The system implements a feedback mechanism where the machine learning model reviews and corrects human-labeled data. The model identifies errors in human annotations and provides corrections, which are then used to update the training data. This feedback loop continuously improves label accuracy without requiring complete manual relabeling, thus resolving the contradiction between reliable labeling and maintaining high model performance.
Solution Approach 2:
The machine learning model performs self-review of the training data by automatically identifying and correcting its own labeling errors. The model uses its own predictions to flag potentially incorrect human annotations and initiates correction processes, enabling the system to self-improve without external intervention for every correction, thereby maintaining both accuracy and productivity.
2Reliability
If all training data is reviewed for label errors, then label quality improves, but time and computational resources are consumed
Solution Approach 1:
The system performs preliminary actions by having the machine learning model pre-review and flag potential label errors before human reviewers examine the data. The model automatically identifies discrepancies and prioritizes them for human review, so that humans only need to verify the flagged items rather than reviewing all data from scratch, thus improving label quality while reducing time consumption.
Solution Approach 2:
The review process is segmented into automated model review and selective human review. The model first processes the entire dataset to identify potential errors, and only the flagged segments requiring human verification are sent to reviewers. This segmentation allows the system to maintain high label quality by catching errors while minimizing the time humans spend on routine checking.
3Productivity
If machine learning models automatically label data, then labeling speed increases, but model inaccuracies and wrong predictions occur
Solution Approach 1:
The system uses feedback mechanisms where the model's own predictions are reviewed and corrected. The model generates labels at high speed, then automatically reviews its work by comparing predictions against each other and flagging inconsistencies. Human reviewers then verify the flagged items, creating a feedback loop that maintains high labeling speed while improving precision by catching and correcting wrong predictions.
Solution Approach 2:
Instead of requiring perfect accuracy for all labels, the system applies partial review to the most critical or uncertain predictions. The model labels all data at high speed, then selectively reviews only the portions with lower confidence scores or potential errors. This partial action approach maintains high overall labeling speed while improving precision for the most important labels, accepting that some low-confidence labels may remain unreviewed but minimizing their impact.
Data Source
AI summary
Labels are often over labeled by machine-learning models and under labeled by human labelers. A solution to the over and under labeling problem is to have both a machine-learning model and a human label a document, then send the document to a parser to determine the discrepancies. The discrepancies are then presented to a human to review and decide whether the machine-learning model identified labels are labels. The feedback is then given to the machine-learning model for further improvement in its confidence calculations which via a confidence threshold determine if the identified labels are presented.