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The deterministic subspace method for constructing classifier ensembles
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Forming classifier ensembles with deterministic feature subspaces

Python code for forming classifier ensembles with deterministic feature subspace approach. Contains deterministic subspace classifier compatible with scikit-learn interface and tools necessary to easily repeat conducted experiments.

More details about the method, results of experiments and related papers can be found at



Tested on Python 2.7.9. Remaining packages used are enclosed in requirements.txt.


To download necessary datasets and create databases in which results will be stored go to main project directory and execute


Results reported in last paper were obtained on precalculated folds, enclosed in this repository. If you want to evaluate different set of folds, you can run

(optional) python

Scheduling trials

Experiment was designed to be run from several processes at once. Because of that queue of pending trials has to be filled first.

By default, all trials described in the last paper will be run. If you want to change that, you can modify accordingly. After that, execute


Running experiment

Main script will try to pull pending trials and evaluate them as long as they are present.


Exporting results to CSV file

After the experiment is done, you can convert results to CSV format by running

python -c 'import database; database.export()'

You can’t perform that action at this time.