Active Learning Data Selection by Uncertainty Scoring
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Solution Overview
Problem
Conventional machine learning training methods are inefficient due to the time-consuming and resource-intensive process of selecting and labeling uncertain data examples, which can lead to prohibitively long training times for large datasets.
Innovation Solution
A computing system is configured to accelerate machine learning processes by selecting a subset of data examples based on their uncertainty levels, updating the machine learning model as examples are labeled, and rescore the complete dataset to continuously select the next batch of examples for labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human users label all data examples in a large dataset, then the machine learning model can be trained comprehensively, but the training process becomes prohibitively time-consuming and resource-intensive
Solution Approach 1:
The patent segments the large dataset into multiple batches and processes them iteratively. Instead of labeling all examples at once, the system divides the data into manageable batches, labels examples from the current batch, updates the model, and then moves to the next batch. This segmentation allows comprehensive training while reducing the time required for each iteration.
Solution Approach 2:
The patent implements preliminary uncertainty scoring of all data examples before labeling begins. By pre-calculating uncertainty scores based on model confidence, the system identifies and prioritizes the most uncertain examples for labeling in each batch. This preliminary action ensures that labeling resources are allocated efficiently to examples that will provide the most benefit for model improvement.
2Adaptability or versatility
If the system updates uncertainty scores for every data example after each labeling, then the model can adapt to new information, but the process becomes computationally expensive and creates lag
Solution Approach 1:
The patent segments the dataset into batches and updates uncertainty scores batch-by-batch rather than individually for every example. This segmentation reduces the computational burden at each step while still maintaining comprehensive model adaptation. The system processes a manageable number of examples per batch, updating scores and model parameters in incremental steps.
Solution Approach 2:
The patent implements periodic updates of uncertainty scores and model parameters at the end of each batch processing cycle. Instead of continuously updating after every single labeling action, the system performs updates periodically at batch boundaries. This periodic action reduces computational complexity and eliminates processing lag while maintaining effective model adaptability.
3Productivity
If the system selects data examples based on uncertainty levels, then labeling efficiency improves, but determining uncertainty scores for all examples becomes time-consuming
Solution Approach 1:
The patent performs preliminary uncertainty scoring of the entire dataset before the labeling process begins. By pre-calculating uncertainty scores for all examples based on initial model predictions, the system establishes a prioritization list that guides batch selection. This preliminary action enables efficient labeling to proceed without time-consuming uncertainty calculations during the actual labeling process.
Solution Approach 2:
The patent segments the dataset into batches and processes uncertainty scoring at the batch level rather than requiring individual example evaluation during labeling. This segmentation allows the system to manage computational resources more effectively, scoring uncertainties in manageable increments and selecting examples from pre-scored batches, thereby improving labeling efficiency while controlling time requirements.
Data Source
AI summary
Systems and methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process.