  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.
