This is the official implementation of our paper titled "HFMOEA: A Hybrid Framework for Multi-objective Feature Selection" published in Journal of Computational Design and Engineering, Oxford.
To install the required libraries run the following in the command prompt:
pip install -r requirements.txt
Run the following code in Command Prompt:
python main.py --path path/to/file/csv_name.csv
By default it is assumed that the csv file contains no headers. But if it does, then add the argument --csv_header True in the code above, otherwise an error will be triggered. All csv files must have the class labels in the last column of the file as integer/float values.
Other available arguments are listed as follows:
popsize: Population Size (Note: must be equal to or more than 10, since 10 filter methods are used to initialize part of the population- refer to our paper for more details)generations: Number of generations for the HFMOEA algorithmmutation: Percentage of mutationtopk: "topk" number of features (Please refer to our paper for more details)save_fig: Whether the plots are to be saved or not
If you find our repository useful, please consider citing our paper:
@article{kundu2022hfmoea,
title={HFMOEA: A Hybrid Framework for Multi-objective Feature Selection,
author={Kundu, Rohit and Mallipeddi, Rammohan},
journal={Journal of Computational Design and Engineering},
year={2022},
publisher={Oxford University Press}
}