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 …

challenge compression cs.lg elastic expansion feature features feature selection framework free leads performance transformation via