Multi-label Active Learning via Selective Sample-Label Pair Annotation
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Solution Overview
Problem
Conventional active learning methods are inefficient for multi-label settings, requiring excessive human interaction and time due to the need for manual labeling of multiple labels per sample, as they fail to leverage the rich correlations among labels.
Innovation Solution
The proposed method involves selecting sample-label pairs for labeling, using a Bayesian classifier with a Kernelized Maximum Entropy Model and Expectation Maximization algorithm to model label correlations, thereby reducing the need for full labeling and minimizing classification error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional active learning methods are used for multi-label settings, then the machine can be trained with labeled samples, but excessive human interaction and time are required due to the need for manual labeling of multiple labels per sample
Solution Approach 1:
The patent applies partial action by selecting only the most informative sample-label pairs for manual annotation rather than labeling all samples. The active learning algorithm identifies and selects a subset of training samples that maximizes information gain, thereby reducing the total number of labels that need to be manually created while maintaining model performance.
Solution Approach 2:
The patent introduces an intermediary active learning algorithm that acts as a mediator between the full set of training samples and the manual labeling process. This intermediary selectively identifies which sample-label pairs should be annotated, filtering out redundant samples and reducing the burden on human annotators.
2Reliability
If manual labeling of all training samples is performed, then complete training data is obtained, but significant and expensive human interaction is required
Solution Approach 1:
The patent applies partial action by selecting only the most informative sample-label pairs for manual annotation rather than labeling all samples. The active learning algorithm identifies and selects a subset of training samples that maximizes information gain, thereby reducing the total number of labels that need to be manually created while maintaining model performance.
Solution Approach 2:
The system employs self-service mechanisms where the active learning algorithm automatically evaluates the value of each sample-label pair and makes selection decisions without requiring human intervention for every sample. The algorithm autonomously identifies which samples provide the most information, reducing the need for exhaustive manual labeling.
3Productivity
If a reduced subset of training samples is labeled to save time and expense, then labeling effort is decreased, but the classification function may be significantly compromised
Solution Approach 1:
The patent implements feedback mechanisms where the active learning algorithm continuously evaluates the impact of selected sample-label pairs on model performance. By using metrics such as information gain and uncertainty sampling, the algorithm provides feedback on which samples will most improve the classification function, ensuring that the reduced subset of labeled samples maintains high model quality.
Solution Approach 2:
The patent changes the selection parameters from random or uniform sampling to intelligent sampling based on information theoretic measures. By optimizing the selection criteria to maximize information gain and minimize uncertainty, the system ensures that the reduced subset of labeled samples provides maximum benefit to the classification function.
Data Source
AI summary
Multi-label active learning may entail training a classifier with a set of training samples having multiple labels per sample. In an example embodiment, a method includes accepting a set of training samples, with the set of training samples having multiple respective samples that are each respectively associated with multiple labels. The set of training samples is analyzed to select a sample-label pair responsive to at least one error parameter. The selected sample-label pair is then submitted to an oracle for labeling.