Zhou Y, Zhang W, Kang J, et al. A problem-specific non-dominated sorting genetic algorithm for supervised feature selection[J]. Information Sciences, 2021, 547: 841-859.
Change the dataset name in line12 of main.m, then execute main.m to start training.
If you want to run multiple rounds in a single run, change the value of t_max in line 23.
After training, the result files result_members.txt and round_cost.out will be generated.
If you used the datasets or code, please cite our article:
@article{zhou2021problem,
title={A problem-specific non-dominated sorting genetic algorithm for supervised feature selection},
author={Zhou, Yu and Zhang, Wenjun and Kang, Junhao and Zhang, Xiao and Wang, Xu},
journal={Information Sciences},
volume={547},
pages={841--859},
year={2021},
publisher={Elsevier}
}