Certainty-Based Classification Networks for Reliable Prediction
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Solution Overview
Problem
Classification networks, such as those used in safety-critical systems like autonomous vehicles, often fail to distinguish between correct and incorrect predictions, leading to potential fatal errors due to hard and soft errors, and out-of-distribution inputs, resulting in suboptimal prediction accuracy.
Innovation Solution
An ensemble of classification networks that incorporate a certainty-based approach, using main and expert classifiers to determine the confidence level of predictions, allowing for the differentiation between certain and uncertain predictions, and implementing additional processing for uncertain predictions to reduce incorrect classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If classification networks provide the best prediction based on input data and training methodology, then prediction speed is improved, but prediction accuracy deteriorates due to inability to distinguish correct from incorrect predictions
Solution Approach 1:
The classification network output is segmented into two distinct components: a predicted class label and a certainty score. This segmentation allows the system to separately evaluate both the prediction result and its confidence level, enabling downstream systems to handle certain and uncertain predictions differently, thereby improving overall reliability without sacrificing prediction speed.
Solution Approach 2:
A certainty score acts as an intermediary between the classification network and the decision-making system. This intermediary provides additional information about prediction reliability, allowing the system to make more informed decisions about which predictions to accept, reject, or flag for further review, thus improving accuracy while maintaining efficient processing.
2Device complexity
If classification networks process all predictions uniformly, then device complexity is reduced, but harmful factors increase due to undetected incorrect predictions in safety-critical systems
Solution Approach 1:
The system dynamically adjusts its processing based on the certainty score. Certain predictions follow a simple uniform processing path, while uncertain predictions trigger additional validation or review processes. This dynamic approach allows the system to maintain low complexity for the majority of cases while providing enhanced safety checks when needed, reducing harmful factors without significantly increasing overall system complexity.
3Ease of operation
If classification networks operate without certainty assessment, then ease of operation is improved, but loss of information occurs due to inability to identify uncertain predictions
Solution Approach 1:
The classification network automatically generates and attaches a certainty score to each prediction without requiring external intervention or complex additional processing. This self-service approach maintains operational simplicity while capturing valuable confidence information that would otherwise be lost, enabling better downstream decision-making.
Data Source
AI summary
A certainty-based prediction apparatus and method are provided. A plurality of main classifier (MC) modules each predict an MC predicted class based on input data, and determine an MC certainty. Each MC module processes a pre-trained, machine learning main classifier having at least one expert class and a plurality of non-expert classes. An expert classifier (EC) module associated with each expert class predicts an EC predicted class based on the input data. Each EC module processes a pre-trained, machine learning expert classifier having two classes including an associated expert class and a residual class that includes any non-associated expert classes and the plurality of non-expert classes. A final predicted class decision module determines a final predicted class and a final certainty based on each MC predicted class, each MC certainty and each EC predicted class. The final predicted class and the final certainty are output.