Hybrid Classifier Routing Low Confidence Items to Humans
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Solution Overview
Problem
Automated classifiers often struggle with classifying content items that are significantly different from their training sets, leading to low classification accuracy, and require significant human intervention for retraining, which is costly and inefficient.
Innovation Solution
Implementing a hybrid classification technique that uses automated classifiers to identify content items with high confidence and sends those with low confidence to human classifiers for retraining, minimizing human involvement while improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated classifiers are used to classify all content items, then productivity is improved, but classification accuracy deteriorates for items significantly different from training sets
Solution Approach 1:
The patent introduces a confidence score as an intermediary metric to mediate between automated classification and human review. The automated classifier assigns confidence scores to each classification, and items with low confidence scores are routed to human classifiers. This intermediary mechanism allows the system to maintain high productivity for confident classifications while ensuring accuracy for uncertain cases without requiring human review of all items.
2Measurement precision
If human classifiers are used to retrain automated classifiers, then classification accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The patent applies partial action by having human classifiers review only a subset of content items - specifically those with low confidence scores - rather than reviewing all classified items. This selective approach allows the system to improve classification accuracy for problematic cases while minimizing the time and cost investment required for human involvement in the retraining process.
3Measurement precision
If human classifiers review all content items, then classification accuracy is improved, but productivity deteriorates
Solution Approach 1:
The confidence score serves as an intermediary that automatically filters content items, routing only those needing human review to human classifiers. This intermediary mechanism enables the system to maintain high productivity by having human classifiers review only a small fraction of total items (those with low confidence scores), while still achieving high overall accuracy through targeted human intervention.
4Adaptability or versatility
If automated classifiers are trained on large data sets, then adaptability is improved, but the complexity of the training process increases
Solution Approach 1:
The patent implements a dynamic training process where the automated classifier continuously adapts based on feedback from human classifiers. Rather than requiring complex upfront training on massive datasets, the system dynamically improves by incorporating human-reviewed examples into the training set, allowing the classifier to adapt to new content types and categories over time with progressively simpler incremental training cycles.
Data Source
AI summary
Many computing scenarios involve the classification of content items within one or more categories. The content item set may be too large for humans to classify, but an automated classifier (e.g., an artificial neural network) may not be able to classify all content items with acceptable accuracy. Instead, the automated classifier may calculate a classification confidence while classifying respective content items. Content items having a low classification confidence may be sent to a human classifier, and may be added, along with the categories identified by the human classifier, to a training set. The automated classifier may then be retrained using the training set, thereby incrementally improving the classification confidence of the automated classifier while conserving the involvement of human classifiers. Additionally, human classifiers may be rewarded for classifying the content items, and the costs of such rewards may be considered while selecting content items for the training set.