Classification with Strategically Withheld Data
This repo reproduces the paper Classification with Strategically Withheld Data. The paper appeared in AAAI 2021, GAIW 2021 and IML 2020. This repo requires Python 3.7.
Downloading the dataset
3 of the 4 datasets are directly retrieved from the internet. One of the dataset needs to be downloaded from the UCI ML Repo.
Running the experiment
To run the experiment
python credit.py <dataset> <fraction> <balanced-flag>
where <dataset> can be "australia", "germany", "poland" or "taiwan", specifying the dataset to run the experiment on; <fraction> can be any value between 0 and 1 (in our work, we do 0.0, 0.1, 0.2, 0.3, 0.4, 0.5), specifying the fraction of data missing for non-strategic reasons; <balanced-flag> can be "bal" or blank, specifying whether or not to balance the dataset before experiement.
To reproduce the paper, run with each of the 4x6x2=48 specifications.
Plotting the results
To generate all tables and figures in tex and png:
python parse.py