P. Domingos proposed a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. The procedure, called MetaCost, treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it.
multilabel-learn: Multilabel-Classification Algorithms
Cost-Sensitive Multi-Label Classification
Deepfake faces detection from forged videos where used explainable AI for models' robustness as well as cost sensitive methods for mitigating dataset imbalance problem
Cost-Sensitive Multi-Class Active Learning
Implementation of cost sensitive KNN algorithm described in Qin, et al, 2013
Weka implementation of the cost-sensitive decision forest algorithm CSForest.
Calculate cost based metrics about data based on the number of positive and negative data points.
Amortized Nonmyopic Bayesian Optimization in the Dynamic Cost Settings
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