Metric-Specific Thresholds for Binary Classification Accuracy
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Solution Overview
Problem
Existing methods for binary classification of digital objects face challenges in accurately determining metric-specific thresholds for nested metrics, leading to potential bias and decreased quality of online services.
Innovation Solution
A server configured to execute a classification engine that applies a target combination of metric-specific thresholds to predictions made by nested metrics for binary classification of digital objects, using an iterative validation process to select optimal thresholds based on precision and recall parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single threshold is used for all metrics, then the classification process is simple, but the classification accuracy deteriorates due to inability to account for metric-specific characteristics
Solution Approach 1:
The patent divides the single threshold parameter into multiple metric-specific thresholds, allowing each metric to have its own optimized threshold value. This segmentation enables the system to account for the different characteristics and importance of each metric while maintaining manageable complexity through systematic threshold determination methods.
Solution Approach 2:
The patent changes the threshold parameter from a single fixed value to multiple adjustable metric-specific values. This parameter change allows the classification system to adapt to the specific characteristics of different metrics, thereby improving classification accuracy while maintaining systematic control through automated determination methods.
2Measurement precision
If metric-specific thresholds are determined through iterative validation, then the classification accuracy improves, but the computational time and complexity increase
Solution Approach 1:
The patent performs preliminary iterative validation to determine optimal metric-specific thresholds before actual classification operations. By pre-determining the thresholds using validation datasets and performance metrics, the system avoids repeated iterative calculations during runtime, thus improving classification accuracy while minimizing computational time loss during actual use.
Solution Approach 2:
The patent implements a feedback mechanism where classification performance is evaluated using validation datasets and performance metrics (such as precision and recall). This feedback is used to iteratively adjust and optimize the metric-specific thresholds, ensuring high classification accuracy while allowing the system to learn from past performance to reduce future computational overhead.
3Ease of manufacture
If traditional classification methods are used, then the implementation is straightforward, but bias in classification results increases
Solution Approach 1:
The patent applies different threshold values to different metrics based on their specific characteristics and importance to the classification task. This local quality approach ensures that each metric contributes appropriately to the final classification decision, reducing bias by accounting for the varying reliability and relevance of different metrics rather than treating them all equally.
Solution Approach 2:
The patent changes from a uniform threshold parameter to multiple metric-specific threshold parameters, allowing the system to adjust for biases in individual metrics. This parameter change enables more fair and reliable classification by giving appropriate weight to each metric based on its specific characteristics, while the automated determination process maintains implementation feasibility.
Data Source
AI summary
Method and server for determining a target combination of metric-specific thresholds to be used with a plurality of nested metrics for performing binary classification of a digital object are disclosed. The method includes acquiring object-specific validation datasets, and a plurality of nested metrics thereon, thereby generating a plurality of prediction values. During a first iteration, the server compares predictions values against a first combination of metric-specific thresholds and generates first precision parameters and first recall parameters for the first iteration. During a second iteration, the server adjusts one of the first combination thereby generating a second combination, compares the predictions values against the second combination, and generates second precision parameters and second recall parameters for the second iteration. The method includes selecting, by the one of the first combination and the second combination as the target combination of metric-specific thresholds.