EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features
This work addresses the challenge of efficient and flexible feature selection for machine learning practitioners, offering a model-free approach that outperforms model-aware methods, though it is incremental as it builds on existing model-free techniques.
The paper tackled the problem of poor performance in model-free feature selection by proposing EasyFS, a framework that uses elastic transformation and redundancy measurement to model feature interrelationships, achieving up to 10.9% better performance in regression and 5.7% in classification while saving over 94% of time compared to state-of-the-art methods.
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.