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Found 5 papers, 2 papers with code
Probability-driven scoring functions in combining linear classifiers
This research is aimed at building a new fusion method dedicated to the ensemble of linear classifiers.
Relearning ensemble selection based on new generated features
The ensemble methods are meta-algorithms that combine several base machine learning techniques to increase the effectiveness of the classification.
Linear Classifier Combination via Multiple Potential Functions
A vital aspect of the classification based model construction process is the calibration of the scoring function.
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels
In the classification of a class imbalance dataset, the performance measure used for the model selection and comparison to competing methods is a major issue.
Combination of linear classifiers using score function -- analysis of possible combination strategies
In this work, we addressed the issue of combining linear classifiers using their score functions.
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