Adaptive Covariate Acquisition for Minimizing Total Cost of Classification

Python 3 implementation for adaptive (dynamic) acquisition of covariates based on the cost of each covariate and potential decrease of misclassification costs.
For convenience 4 preprocessed datasets are accompanied:
Pima Diabetes (pima_5foldCV), Breast Cancer (breastcancer_5foldCV), Heartdisease (heartDiseaseWithMissing_5foldCV), PhysioNet (pyhsioNetWithMissing_5foldCV)

Steps for reproducing the experimental results of proposed method and baselines

1. Initialization: Set all path constants in "constants.py" and "commonVisualizationDefinitions.py"

2. Training: run all evaluation measures and save results. For using the group lasso penalty (default) use, e.g.

$ python precalculateAllNonLinearL1FeatureSets.py pima_5foldCV

For using forward-selection (with asymmetric costs) use, e.g.

$ python precalculateAllGreedyFeatureSets.py pima_5foldCV asymmetricCost

2. Run evaluation: run all evaluation measures and save results ("greedy" = forward-selection method, "nonLinearL1" = group lasso method), e.g.:

$ python getOptimalSequence_totalCosts.py pima_5foldCV asymmetricCost nonLinearL1

3. Visualize Results: Specify the dataset and experimental setting in the beginning of showAllResults.py and run:

$ python showAllResults.py

Implementation Details

For learning the non-linear classifiers we use part of the implementation from the pygam package: https://pygam.readthedocs.io/en/latest/