QBSO-FS : a Reinforcement Learning based Bee Swarm Optimization metaheuristic for Feature Selection

This is the official Python implementation of the paper:

QBSO-FS : a Reinforcement Learning based Bee Swarm Optimization metaheuristic for Feature Selection https://doi.org/10.1007/978-3-030-20518-8_65

by Sadeg et al. (IWANN2019)

Requirements

1. Download current version of the repository. ( Or refer to point 3 if you want to use a Jupyter Notebook )

git clone https://github.com/amineremache/qbso-fs.git

2. Install the dependencies in the requirements.txt file.

pip install -r requirements.txt

or

pip install numpy scikit-learn pandas xlsxwriter

3. If you don't want to use the code locally, or you want to run it from a notebook, you can run one of the notebooks present at ./notebooks/.

The code was tested on Ubuntu 16 and 18, Windows 10 with Python 3.6 and 3.7.

Running the code

To run the code, just go to main.py. For now, only KNN is implemented, but you can add your own classifier in fs_problem.py file.

Citation

If you use this work, please cite:
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature Selection, Sadeg S., Hamdad L., Remache A.R., Karech M.N., Benatchba K., Habbas Z, IWANN, 2019.