This is the code for the paper on "Robust Instance-Dependent Cost-Sensitive Classification".
Citation: De Vos, Simon, Toon Vanderschueren, Tim Verdonck, and Wouter Verbeke. 2023. “Robust Instance-Dependent Cost-Sensitive Classification.” Advances in Data Analysis and Classification, January. https://doi.org/10.1007/s11634-022-00533-3.
Contact the author at simon.devos@kuleuven.be.
The creditcard transaction data can be found here: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
The .csv file should be placed in the data folder as "data\Kaggle Creditcard Fraud\creditcard.csv". You can replace the now empty creditcard.csv file.
Run overview.py to execute the experiments as described in the paper.
Settings can be adapted in overview.py:
- Set DIR variable to your custom result folder
- Specify experimental configuration
- Default settings:
5-fold cross-validation, 2 repeats
Toy example on synthetic data is generated and displayed
Three classifiers are trained: logit, cslogit, r-cslogit
evaluators: traditional, AUC, Savings
The code for cslogit is a Python version of the original cslogit by Sebastiaan Höppner et al..