Reproducible code and experimental scripts accompanying the paper "The impact of data difficulty factors on classification of imbalanced and concept drifting data streams".

The repository contains generator source code, experimental scripts including data stream generation scripts, and detailed results of the classification experiments discussed in "The impact of data difficulty factors on classification of imbalanced and concept drifting data streams". The repository is divided into the following folders:

• the main folder contains a (bash) makefile, list of python modules used to process experimental results (python.requirements), and a script for generating various streams discussed in the accompanying research paper
• src contains Java source code compatible with MOA; to use the data stream generator and classifiers attach this code to your project or include it while running moa.jar
• scripts contains python source code use to post-process experimental results
• synthetic-streams contains cached experiment results in the form of: 1) csv files containing classifier performance values over time, 2) classifier performance plots, 3) classifier perofmance values averaged over entire streams
• real-streams contains cached experiment results for real-world datasets

To recreate data stream plots with the same options as in the paper, run:

python plot_metrics.py "../synthetic-streams/evaluations" "../synthetic-streams/plots" "Recall" "G-Mean" -s 10 -a 20 -d 70000 100000 -r -o ESOS_ELM OOB UOB OB VFDT
python plot_metrics.py "../real-streams/evaluations" "../real-streams/plots" "Recall" "G-Mean" -r -o ESOS_ELM OOB UOB OB VFDT

If you have trouble reproducing the experiments or have any comments/suggestions, feel free to write at dariusz.brzezinski (at) cs.put.poznan.pl