Interactive Classifier Threshold Configuration for Detection Models
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Solution Overview
Problem
Machine learning detection systems face challenges due to large class imbalances, leading to issues like false alarms overload and alarm desensitization, and users struggle to configure classifier detection thresholds effectively.
Innovation Solution
A system and method that allow users to interactively configure classifier detection thresholds for deployed detection models by providing performance information such as false alarm gain, precision, and recall values, enabling informed threshold adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the classifier detection threshold is set to improve detection sensitivity, then recall value increases, but false alarm gain increases leading to false alarms overload
Solution Approach 1:
The system enables dynamic adjustment of the classifier detection threshold parameter. Users can modify the threshold value to change the balance between recall and false alarm gain, allowing optimization based on specific operational requirements and risk tolerances.
Solution Approach 2:
The system provides feedback mechanisms that allow users to observe the impact of threshold changes on performance metrics (recall, precision, false alarm gain). This feedback loop enables users to iteratively adjust the threshold to achieve desired performance characteristics.
2Object-generated harmful factors
If the classifier detection threshold is set to reduce false alarms, then false alarm gain decreases, but recall value decreases leading to missed detections
Solution Approach 1:
The system enables dynamic adjustment of the classifier detection threshold parameter. Users can modify the threshold value to change the balance between false alarm gain and recall, allowing optimization based on specific operational requirements and risk tolerances.
3Ease of operation
If the detection model uses default classifier thresholds, then deployment is simplified, but user ability to reflect their values regarding costs and risks is limited
Solution Approach 1:
The system transitions from static default thresholds to dynamic user-configurable thresholds. The threshold parameter becomes adjustable, allowing the detection model to adapt to different user preferences, cost structures, and risk tolerances while maintaining ease of deployment through preserved defaults.
Solution Approach 2:
The system empowers users to self-configure the detection model by allowing them to adjust thresholds based on their own value judgments regarding costs and risks. Users can independently optimize the model for their specific operational context without requiring expert intervention.
Data Source
AI summary
A system includes a memory and a processor. The memory stores instructions, a threshold parameter corresponding to classifier detection thresholds for a trained detection model, and performance information associated with the threshold parameter. The processor executes the instructions to detect classifier detection thresholds set for the trained detection model, and identify for the classifier detection thresholds, false alarm gains, precision values and recall values. The processor outputs a signal that includes the false alarm gains, the precision values, and the recall values.