Machine Learning Classifier Confidence Threshold Control
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Solution Overview
Problem
Machine learning classifiers lack user control over confidence thresholds, leading to inadequate classification accuracy and inefficiency in handling predictions below a certain threshold, which can result in unnecessary manual review and retraining.
Innovation Solution
A machine learning classifier configured to receive test data, apply confidence thresholds to reclassify observations with probabilities below a certain threshold into a 'null' category, and determine precision and coverage values to select optimal thresholds for improved classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the classifier automatically classifies all observations without confidence threshold control, then classification speed is improved, but classification accuracy deteriorates due to low-confidence predictions
Solution Approach 1:
The system changes the parameter of confidence threshold from a fixed default value to a user-configurable parameter. Users can adjust the threshold level based on their specific needs, allowing them to balance between classification speed and accuracy. Higher thresholds improve accuracy by requiring stronger confidence before automatic classification, while lower thresholds improve speed by allowing more automatic classifications.
2Measurement precision
If the classifier uses a high confidence threshold, then classification accuracy is improved, but the number of observations requiring manual review increases
Solution Approach 1:
The system implements dynamic adjustment of confidence thresholds based on observation characteristics. Rather than applying a single static threshold to all observations, the system can adaptively adjust thresholds for different types of observations, allowing high thresholds for critical classifications that require accuracy and lower thresholds for routine classifications where speed is more important.
Solution Approach 2:
The system performs preliminary analysis of observations to identify which ones are likely to meet high confidence thresholds before applying the threshold filter. This allows the system to prepare and pre-process observations that will definitely be automatically classified, reducing the burden of manual review for subsequent processing.
3Loss of information
If the classifier provides detailed probability information for all categories, then information completeness is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the most relevant probability information for display and processing. Rather than presenting all probability values for all categories, the system identifies and extracts the top-k most likely categories and their probabilities, removing unnecessary data while preserving the essential information needed for decision-making.
Solution Approach 2:
The system applies different levels of information detail to different parts of the output based on local needs. For automatically classified observations, only the final classification result is provided. For observations near the confidence threshold or requiring manual review, more detailed probability information is provided to assist reviewers in making informed decisions.
Data Source
AI summary
A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.