Active Learning Dictionary Update for Labeling Termination
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Solution Overview
Problem
Existing active learning methods require significant processing time and man-hours to determine when to terminate labeling tasks, as they lack efficient criteria for stabilizing predictions and evaluating the termination of labeling work.
Innovation Solution
A learning apparatus and method that updates a dictionary used by a classifier, calculates a loss ratio based on labeled samples, and determines whether to update the dictionary using this ratio, allowing for precise termination of labeling tasks by monitoring changes in loss values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active learning is performed by selecting samples with least reliable discrimination results and having users label them, then the classifier's accuracy is improved, but the processing time and man-hours required to determine termination become excessive
Solution Approach 1:
The patent implements a feedback mechanism where the classifier's prediction results are continuously monitored and fed back to determine termination. Specifically, the system calculates prediction reliability metrics (such as entropy or confidence scores) for unlabeled samples and uses this feedback to automatically determine when sufficient samples have been labeled, eliminating the need for excessive manual evaluation and reducing processing time while maintaining accuracy improvement.
2Measurement precision
If active learning is performed by selecting samples with least reliable discrimination results and having users label them, then the classifier's accuracy is improved, but the man-hours required for termination determination become excessive
Solution Approach 1:
The patent enables the system to automatically determine termination of active learning through self-service mechanisms. The classifier autonomously evaluates its own performance by monitoring prediction reliability metrics on unlabeled samples and automatically determines when to stop sampling and labeling, without requiring extensive manual intervention or expert judgment, thereby significantly reducing the operational burden and man-hours required.
3Reliability
If new learning and evaluation are performed to determine termination of active learning, then the termination decision is made, but significant processing time is consumed
Solution Approach 1:
The patent applies partial action by performing only the necessary subset of learning and evaluation operations required for reliable termination determination. Instead of conducting exhaustive re-learning and comprehensive evaluation, the system performs targeted calculations of prediction reliability metrics on a subset of unlabeled samples, achieving sufficient reliability for termination decisions while consuming significantly less processing time.
Data Source
AI summary
A learning apparatus includes: an update unit which updates a dictionary used by a classifier; a calculation unit which calculates, by using a dictionary updated and one or more samples with labeling being samples assigned with labels, a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and a determination unit which determines whether to update the dictionary, by using the loss, wherein, when the determination unit determines to update the dictionary, the update unit updates the dictionary by using the samples with labeling added with a new sample with labeling, and wherein the determination unit determines whether to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling.