Adaptive Oracle Framework for ML Training Data Selection
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Solution Overview
Problem
Current systems for building and maintaining machine learning models that process dynamic data face challenges due to data quality fluctuations, making it difficult and costly to obtain high-quality training data and adapt models to changing data distributions.
Innovation Solution
An adaptive oracle-trained learning framework that leverages a crowd or oracle for automatic generation of high-quality training data and uses active learning to monitor model performance and update training data sets, enabling incremental adaptation of models to maintain data quality and reduce replacement costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-quality training data is obtained through traditional methods (manual verification and labeling by reliable sources), then data quality and accuracy are improved, but time consumption and cost increase significantly
Solution Approach 1:
The system enables self-service by having the model automatically select and label training data instances based on its own predictions and uncertainty measurements. The model identifies high-quality candidates autonomously without requiring manual verification for each instance, thus improving data quality while reducing time consumption through automated self-curation processes.
Solution Approach 2:
Instead of verifying all data instances exhaustively, the system applies partial action by selectively verifying only those instances that the model identifies as high-quality candidates. This targeted approach maintains high data quality standards while significantly reducing the overall time and resources required compared to comprehensive manual verification of entire datasets.
2Measurement precision
If the model is replaced frequently to adapt to changing data distributions, then model accuracy is improved, but replacement costs increase
Solution Approach 1:
The system implements continuous feedback loops where the model's performance on dynamic data is monitored, and this feedback is used to iteratively improve the model through selective training data updates. Rather than complete replacements, the model adapts incrementally by incorporating new high-quality training instances identified through the feedback mechanism, maintaining accuracy while reducing replacement costs.
Solution Approach 2:
The system embraces dynamics by enabling the model to adapt continuously to changing data distributions through incremental updates rather than static, periodic replacements. The model dynamically adjusts its training data set based on current performance metrics and data stream characteristics, allowing it to maintain high accuracy in evolving environments without the high costs associated with frequent complete model replacements.
3Reliability
If comprehensive model replacement is performed to adapt to data quality fluctuations, then model performance is maintained, but computational resources and time are consumed
Solution Approach 1:
The system applies segmentation by dividing the model update process into discrete, manageable components: identifying specific training data instances that need updating, selecting only those high-quality candidates, and incrementally integrating them. This segmented approach maintains model performance reliability by ensuring quality updates while improving system efficiency through targeted, rather than comprehensive, updates.
Solution Approach 2:
The system performs preliminary action by pre-identifying and pre-labeling high-quality training data candidates before they are needed for model updates. This advance preparation involves the model selecting and verifying candidate instances in advance, so when updates are required, they can be executed efficiently without consuming excessive computational resources or time during critical performance periods.
4Measurement precision
If expert human involvement is increased to ensure data quality, then data accuracy is improved, but operational complexity and cost increase
Solution Approach 1:
The system replaces expert human involvement with self-service capabilities where the model autonomously identifies, selects, and labels training data instances based on its own predictions and uncertainty measurements. This automation maintains data accuracy through the model's inherent learning capabilities while dramatically reducing operational complexity by eliminating the need for manual expert verification processes.
Solution Approach 2:
The system substitutes the mechanical process of manual expert verification with an automated computational mechanism. The model uses algorithmic processes to identify and validate training data quality, replacing the need for human experts physically reviewing and labeling data. This substitution maintains data accuracy through sophisticated computational methods while reducing operational complexity by automating what was previously a manual, complex human process.
Data Source
AI summary
In general, embodiments of the present invention provide systems, methods and computer readable media for an adaptive oracle-trained learning framework for automatically building and maintaining models that are developed using machine learning algorithms. In embodiments, the framework leverages at least one oracle (e.g., a crowd) for automatic generation of high-quality training data to use in deriving a model. Once a model is trained, the framework monitors the performance of the model and, in embodiments, leverages active learning and the oracle to generate feedback about the changing data for modifying training data sets while maintaining data quality to enable incremental adaptation of the model.