Machine Learning Uncertainty Quantification via Data Segmentation
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Solution Overview
Problem
Traditional machine learning classifier models struggle to accurately classify data items outside the distribution of their labeled training set, leading to potentially adverse consequences due to overconfidence in incorrect classifications.
Innovation Solution
A machine learning training process that utilizes a combination of labeled, in-scope unlabeled, and out-of-scope unlabeled data items to train analysis models, ensuring accurate classification within the labeled scope and indicating lack of confidence for out-of-scope items, employing multiple sub-models with different classification functions to maintain low confidence in out-of-scope data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional classifier models are used to classify data items, then the model can quickly provide classification predictions, but the model may confidently predict classifications for data items outside the training set distribution leading to incorrect classifications
Solution Approach 1:
The patent segments the training data into three distinct sets: in-distribution labeled data, out-of-distribution unlabeled data, and in-distribution unlabeled data. This segmentation allows the model to learn different characteristics from each set, enabling it to distinguish between familiar and unfamiliar data patterns while maintaining fast classification performance.
Solution Approach 2:
The patent performs preliminary training actions by exposing the model to out-of-distribution unlabeled data before actual classification tasks. This preliminary exposure teaches the model to recognize unfamiliar patterns and associate them with low confidence scores, preventing premature confident misclassifications while preserving rapid inference capabilities.
2Device complexity
If parametric machine learning methods are used to produce confidence intervals, then the model structure is simplified, but the structure assumption is too narrow to accurately model real data
Solution Approach 1:
The patent changes the approach from assuming fixed parametric structures to dynamically adjusting confidence scores based on data distribution characteristics. By introducing uncertainty parameters that adapt to out-of-distribution detection, the system achieves accurate confidence intervals without committing to restrictive parametric assumptions about the underlying data generation process.
3Adaptability or versatility
If nonparametric machine learning methods are used to increase modeling capability, then the model can handle complex real data distributions, but producing confidence intervals becomes difficult and not scalable to large data sets
Solution Approach 1:
The patent extracts the confidence interval computation from the complex nonparametric modeling process by using a separate uncertainty estimation mechanism. This allows the main nonparametric model to focus on accurate classification while a dedicated uncertainty module efficiently computes confidence scores, achieving both high adaptability and scalability on large datasets.
4Reliability
If variational approaches are used to produce confidence intervals, then some structure assumptions are made, but this hybrid approach results in lower accuracy and inability to apply to pure nonparametric models
Solution Approach 1:
The patent implements a dynamic approach where confidence scores are adjusted in real-time based on whether input data matches the training distribution, rather than relying on fixed variational assumptions. This dynamic uncertainty estimation works with any base model architecture, preserving the accuracy benefits of pure nonparametric methods while enabling reliable confidence interval production.
Data Source
AI summary
A method and system assists train a classifier model with a machine learning process. The method and system trains the classifier with a labeled training set and with an unlabeled training set. The method and system trains the classifier model to correctly classify data items that fall within a distribution of the labeled training set. The method and system trains the classifier to indicate a lack of confidence in classification for data items that do not fall within the distribution of the labeled training set.