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Found 3 papers, 1 papers with code
Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models
To tackle this problem, we propose Sample Discrimination based Selection (SDS) to select efficient samples that could discriminate multiple models, i. e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance.
Connecting Software Metrics across Versions to Predict Defects
Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently.
A Feature Subset Selection Algorithm Automatic Recommendation Method
The performance of the candidate FSS algorithms is evaluated by a multi-criteria metric that takes into account not only the classification accuracy over the selected features, but also the runtime of feature selection and the number of selected features.
