Oversampling Imbalanced Test Data for Classifier Evaluation
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Solution Overview
Problem
Classifiers trained on imbalanced data sets face challenges in accurately determining precision and recall metrics due to the high cost and time-consuming nature of manual labeling, leading to inefficient editorial labeling processes and imbalanced sample sets that do not accurately represent the data distribution.
Innovation Solution
An oversampling ratio is calculated based on the imbalance ratio, estimated precision and recall metrics, and production specifications to select a sample set for editorial labeling, ensuring that the sample set accurately represents the data distribution while minimizing the number of labeled data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to obtain labeled test data, then the accuracy of precision and recall metrics is improved, but the time consumption and cost increase prohibitively
Solution Approach 1:
The patent applies partial action by selecting only a sample subset of data points for manual editorial labeling rather than labeling the entire dataset. This allows obtaining sufficient information to estimate precision and recall metrics with acceptable accuracy while significantly reducing the time and cost associated with complete manual labeling. The system determines confidence intervals based on this partial labeling to assess classifier performance.
2Measurement precision
If a large sample set is selected for editorial labeling, then the accuracy of confidence intervals is improved, but the number of data points requiring manual labeling increases
Solution Approach 1:
The system performs self-service by automatically determining the optimal sample size and confidence interval calculations without requiring extensive manual intervention. The classifier itself provides predictions that are used to identify data points for sampling, and the system automatically computes the confidence intervals based on the sampled data, reducing the need for large manual labeling efforts while maintaining statistical rigor.
3Ease of operation
If random sampling is used to select data points, then the selection process is simple, but the sample set does not accurately represent the data distribution when classes are imbalanced
Solution Approach 1:
The patent applies local quality by adjusting the sampling strategy to account for local characteristics of different data classes. When class imbalance is detected, the system modifies the sampling approach to ensure adequate representation of minority classes while maintaining simplicity. This allows the sample set to accurately reflect the overall data distribution without requiring complex sampling procedures.
Data Source
AI summary
One or more computing devices, systems, and/or methods for oversampling for imbalanced test data are provided. A classifier is configured to classify data points as either belonging to a first class or a second class. A determination may be made that the first class and the second class are imbalanced where a first number of data points estimated to be part of the first class is a threshold amount less than a second number of data points estimated to be part of the second class. An oversampling ratio is determined for the first class. The oversampling ratio is used to select a sample set of data points for editorial labeling, where the sampling set of data points comprises a total number of data points below a threshold amount.