Hybrid Data Labeling System for ML Accuracy
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Solution Overview
Problem
Current data labeling methods for neural networks are inefficient and error-prone, requiring extensive human labor and lacking effective management of mixed intelligence between human and machine labelers, which affects dataset quality and predictive model accuracy.
Innovation Solution
A hybrid data labeling method that involves pre-labeling unlabeled data sets by machine learning systems, bifurcating labels into high and low confidence sets, dispatching high confidence sets to machine labelers and low confidence sets to human labelers, merging results, and reviewing differences to store data in a reviewed pool based on error thresholds, thereby optimizing human and machine labor utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human labelers manually label all data objects, then labeling accuracy is improved, but productivity decreases and labor costs increase
Solution Approach 1:
The patent segments the labeling task by confidence level, dividing data objects into high-confidence cases (handled by machine) and low-confidence cases (handled by humans). This segmentation allows the system to leverage automated efficiency for clear cases while reserving human expertise for ambiguous cases, thereby improving overall productivity without sacrificing accuracy.
Solution Approach 2:
The machine learning system performs self-labeling for high-confidence data objects, serving itself without human intervention. This self-service capability handles the majority of clear-cut cases automatically, freeing human labelers to focus only on challenging cases that require human judgment, thus dramatically improving productivity while maintaining accuracy.
2Productivity
If machine learning systems pre-label all data objects, then productivity increases, but measurement precision decreases
Solution Approach 1:
The patent applies different quality standards and handling approaches to different segments of data based on local characteristics (confidence levels). High-confidence data objects receive automated machine labeling with high quality, while low-confidence data objects are routed to human labelers for higher quality processing. This local quality approach ensures overall high accuracy while maintaining high productivity.
Solution Approach 2:
The system implements feedback loops where human-labeled low-confidence cases are used to retrain and improve the machine learning model. This continuous feedback mechanism allows the machine to progressively improve its precision, reducing the number of cases that require human intervention over time, thereby simultaneously improving both productivity and measurement precision.
3Reliability
If all data objects are reviewed by human reviewers, then reliability is improved, but loss of time increases
Solution Approach 1:
Instead of reviewing all data objects, the patent applies partial review action only to low-confidence data objects that were pre-labeled by the machine. High-confidence data objects skip the review stage entirely. This partial action approach maintains dataset quality by reviewing only the necessary cases while dramatically reducing the time loss associated with universal human review.
4Manufacturing precision
If human labelers are used for all labeling tasks, then manufacturing precision is improved, but device complexity decreases, but if machine labelers are used, then productivity increases but manufacturing precision decreases
Solution Approach 1:
The patent creates a universal labeling system that can handle both high-confidence and low-confidence data objects through a single integrated platform. The system automatically routes different types of data to appropriate handlers (machine or human) based on confidence levels, providing multi-functional capability that maintains high label quality while managing system complexity through centralized control.
Data Source
AI summary
A method of hybrid data labeling for machine learning, including receiving multiple unlabeled objects forming an unlabeled data set, pre-labeling the unlabeled data set by a machine learning system to output a pending label data pool, bifurcating the pending label data pool by the machine learning system into high and low confidence sets, dispatching the high confidence set to a machine labeler, dispatching the low confidence set to a human labeler, merging the label sets to return a pre-review label data pool, determining a difference between the pending label data pool and the pre-review label data pool, review labeling the data objects, if the determined difference of the data objects is greater than a predefined error threshold and storing the data objects to a reviewed pool if the determined difference of the data objects is less than and equal to the predefined error threshold.