Dynamic Classifier Threshold Adjustment for False Classification Reduction
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Solution Overview
Problem
Classifiers with fixed threshold values can lead to false positive and false negative classifications, which may result in inefficient processing operations and potential failures in critical applications, as they do not account for specific user characteristics or application contexts.
Innovation Solution
An apparatus and method for dynamically updating the threshold value of classifiers based on feedback data, determining false classifications through events such as negative interactions or repeat inputs, and adjusting the threshold within defined tolerance values to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed threshold value is used in the classifier, then the device complexity is reduced and ease of operation is improved, but the reliability deteriorates due to false positive and false negative classifications
Solution Approach 1:
The patent applies the Dynamics principle by transitioning from a fixed threshold value to a dynamically adjustable threshold that can be updated based on feedback data. The threshold value is modified responsive to detected events such as false positive or false negative classifications, allowing the classifier to adapt to specific user characteristics and application contexts. This dynamic adjustment mechanism resolves the contradiction by improving reliability through context-aware threshold optimization while maintaining relatively simple device architecture.
Solution Approach 2:
The patent implements the Feedback principle by using feedback data from detected events to update the threshold value. When false positive or false negative classifications are detected, the system processes this feedback information and adjusts the threshold accordingly. This closed-loop feedback mechanism enables continuous improvement of classification accuracy without requiring complex retraining processes, thereby resolving the contradiction between reliability and device complexity.
2Adaptability or versatility
If the threshold value is updated dynamically based on feedback data, then the reliability and adaptability are improved, but the device complexity increases
Solution Approach 1:
The patent applies the Self-service principle by enabling the classifier to automatically adjust its own threshold value based on feedback data without requiring external intervention or complex retraining processes. The system monitors its own performance, detects false classifications, and autonomously updates the threshold to improve adaptability to user contexts. This self-service capability achieves personalization while keeping the feedback processing system relatively simple.
Solution Approach 2:
The patent implements the Parameter changes principle by modifying the threshold value parameter in response to detected events and feedback data. Instead of changing the entire computational model, the system adjusts a single critical parameter (the threshold) to achieve adaptability to different user characteristics and application contexts. This focused parameter adjustment achieves versatility while minimizing the increase in device complexity compared to full model retraining.
3Productivity
If a fixed threshold is used, then the ease of manufacture and operation are improved, but the productivity deteriorates due to inefficient processing operations triggered by false classifications
Solution Approach 1:
The patent applies the Preliminary action principle by proactively updating the threshold value before false classifications can cause significant productivity loss. The system continuously monitors feedback data and adjusts the threshold in advance to prevent inefficient processing operations. This preventive approach improves productivity by reducing the frequency of false positives and false negatives, while the automated nature of the updates maintains ease of operation.
Solution Approach 2:
The patent implements the Feedback principle by using information from false classification events to automatically adjust the threshold value. The feedback loop processes information about inefficient processing operations and modifies the threshold to prevent recurrence. This automated feedback mechanism improves productivity by reducing wasted processing operations while maintaining ease of operation through self-adjustment without requiring manual threshold configuration expertise.
Data Source
AI summary
Example embodiments may relate to an apparatus, method and/or computer program for the updating, or tuning, of classifiers. For example, the method may comprise receiving data indicative of a positive or negative classification based on comparing an output value, generated by a computational model responsive to an input data, with a threshold value which divides a range of output values of the computational model into positive and negative classes of output values. A positive or a negative classification may be usable by the apparatus, or another apparatus, to trigger one or more processing operations. Other operations may comprise determining that the positive or negative classification is a false classification based on one or more events detected subsequent to generation of the output value and updating the threshold value responsive to determining that the positive or negative classification is a false classification.