Dynamic Confidence Thresholds for Machine Learning Model Training
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Solution Overview
Problem
Existing machine learning data analysis systems are ill-suited to accurately, efficiently, and consistently perform predictive data analysis, particularly in domains with high-dimensional categorical feature spaces and high cardinality, and face challenges with expensive and laborious label data generation.
Innovation Solution
The implementation of machine learning data manipulation, training, and prediction techniques that utilize dynamically defined confidence thresholds through multi-armed bandit modeling, enabling reinforcement learning for improved model training and prediction accuracy with reduced computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning systems are used for predictive data analysis, then the systems can perform basic predictions, but they are computationally inefficient and inaccurate for high-dimensional categorical feature spaces with high cardinality
Solution Approach 1:
The patent implements dynamic confidence thresholds that are adjusted during the reinforcement learning process based on model performance and exploration needs. The threshold is not fixed but evolves adaptively, allowing the system to balance between exploitation of known good predictions and exploration of potentially better ones, thereby improving both accuracy and efficiency in high-dimensional spaces
Solution Approach 2:
The system changes the confidence threshold parameter dynamically during training and inference. By adjusting this parameter based on the multi-armed bandit model's exploration-exploitation balance, the system optimizes prediction accuracy while maintaining computational efficiency, resolving the contradiction between precision and productivity
2Reliability
If manual label data generation is used for training machine learning models, then the training data can be accurately labeled, but the process is expensive and laborious
Solution Approach 1:
The machine learning model serves itself by generating pseudo-labels for unlabeled data through reinforcement learning. The system uses its own predictions, filtered through dynamic confidence thresholds, to create training labels automatically, eliminating the need for expensive manual labeling while maintaining reliability through the RL-based quality control mechanism
Solution Approach 2:
The system implements feedback loops where model predictions are evaluated against confidence thresholds, and successful predictions are used to generate pseudo-labels that feed back into training. This self-reinforcing mechanism improves label accuracy over time while dramatically reducing labeling time and cost
3Manufacturing precision
If high confidence thresholds are used for data augmentation, then the quality of synthetic labeled data is high, but the quantity of available training data is reduced
Solution Approach 1:
The confidence threshold is made dynamic and adaptive rather than fixed. During different phases of training and for different data samples, the threshold adjusts automatically based on the multi-armed bandit model's exploration-exploitation balance, allowing the system to maintain high quality while maximizing the quantity of usable synthetic training data
Solution Approach 2:
The system changes the confidence threshold parameter based on the reward indicator from the multi-armed bandit model. When exploration is prioritized, lower thresholds allow more data to be augmented; when exploitation is prioritized, higher thresholds ensure higher quality. This dynamic parameter adjustment resolves the contradiction between quality and quantity
Data Source
AI summary
Various embodiments of the present disclosure provide reinforcement learning for machine learning using dynamic confidence thresholds. In one example, an embodiment provides for generating a plurality of training datasets for a machine learning model by augmenting a labeled dataset for the machine learning model with a synthetic labeled dataset, generating a plurality of retrained model versions of the machine learning model based on the plurality of training datasets, generating a reward indicator for a retrained model version of the plurality of retrained versions of the machine learning model based on a comparison between a validation dataset for the machine learning model and a respective output dataset for the retrained model version, and modifying the defined confidence threshold based on the reward indicator for the retrained model version to generate a modified confidence threshold for the machine learning model.