EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features
<|reference_start|>EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features: Traditional model-free feature selection methods treat each feature independently while disregarding the interrelationships among features, which leads to relatively poor performance compared with the model-aware methods. To address this challenge, we propose an efficient model-free feature selection framework via elastic expansion and compression of the features, namely EasyFS, to achieve better performance than state-of-the-art model-aware methods while sharing the characters of efficiency and flexibility with the existing model-free methods. In particular, EasyFS expands the feature space by using the random non-linear projection network to achieve the non-linear combinations of the original features, so as to model the interrelationships among the features and discover most correlated features. Meanwhile, a novel redundancy measurement based on the change of coding rate is proposed for efficient filtering of redundant features. Comprehensive experiments on 21 different datasets show that EasyFS outperforms state-of-the art methods up to 10.9\% in the regression tasks and 5.7\% in the classification tasks while saving more than 94\% of the time.<|reference_end|>
@article{lv2024easyfs:,
    title={EasyFS: an Efficient Model-free Feature Selection Framework via Elastic
  Transformation of Features},
    author={Jianming Lv, Sijun Xia, Depin Liang, Wei Chen},
    journal={arXiv preprint arXiv:2402.05954},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05954},
    primaryClass={cs.LG}
}