Selective-Label Decision Policy With Confidence Trade-Off Control
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Solution Overview
Problem
Existing machine learning models struggle with decision-making under selective labels, where outcomes are not observed for one decision, leading to inaccurate predictions and inefficiencies in applications like pharmaceutical treatments and license suspensions.
Innovation Solution
A computer-implemented method using selective labels that balances short-term learning and long-term utility by employing a machine learning policy with a trade-off parameter, incorporating a success probability model, confidence model, and discount factor to optimize decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a machine learning model is trained with selective labels where outcomes are not observed for one decision, then the model can be applied to high-stakes decision-making applications, but the decision accuracy deteriorates because there is no way to determine if the decision would have been effective
Solution Approach 1:
The system implements feedback by tracking outcomes for accepted decisions and using this information to update the machine learning model. The feedback loop allows the model to learn from observed outcomes while handling the selective label problem where rejected decisions do not have observed outcomes.
Solution Approach 2:
The patent changes the parameter representation by using a confidence value parameter that reflects the uncertainty associated with selective labels. This parameter adjustment allows the model to account for the missing outcome information in a mathematically rigorous way.
2Measurement precision
If the machine learning model collects more data to improve decision accuracy, then the prediction precision improves, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively collecting outcomes only for accepted decisions rather than all decisions. This partial observation strategy is sufficient to train the model effectively while avoiding the overhead of collecting and processing data for all decisions, including those where outcomes would not be observed anyway.
Solution Approach 2:
The model performs preliminary classification to identify high-confidence predictions that do not require outcome observation. This preliminary action allows the system to make accurate decisions quickly for certain cases without waiting for outcome data.
3Measurement precision
If the machine learning model performs extensive training to improve decision accuracy, then the outcome precision improves, but the training efficiency deteriorates due to increased computational overhead
Solution Approach 1:
The training process uses partial action by leveraging only the observed outcomes from accepted decisions. This partial data set is sufficient for effective training under selective labels, avoiding the computational overhead of processing or imputing data for rejected decisions where outcomes are not observed.
Solution Approach 2:
The system uses a confidence value parameter that can be quickly computed and updated without requiring extensive retraining. This lightweight parameter allows the model to adapt to new information efficiently without the high computational cost of full model retraining.
Data Source
AI summary
A computer-implemented method of decision-making using selective labels, includes receiving a conditional success probability value of a feature associated with an entity. A confidence value of the received success probability value is received. A parameter value that is a trade-off between a short-term learning and a long-term utility is selected. A decision is rendered to accept or reject the feature associated with the entity according to a machine learning policy.