A model-free feature selection technique of feature screening and random forest based recursive feature elimination

• URL: http://arxiv.org/abs/2302.07449v1
• Date: Wed, 15 Feb 2023 03:39:16 GMT
• Title: A model-free feature selection technique of feature screening and random forest based recursive feature elimination
• Authors: Siwei Xia and Yuehan Yang
• Abstract summary: We propose a model-free feature selection method for ultra-high dimensional data with mass features. We show that the proposed method is selection consistent and $L$ consistent under weak regularity conditions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a model-free feature selection method for ultra-high dimensional data with mass features. This is a two phases procedure that we propose to use the fused Kolmogorov filter with the random forest based RFE to remove model limitations and reduce the computational complexity. The method is fully nonparametric and can work with various types of datasets. It has several appealing characteristics, i.e., accuracy, model-free, and computational efficiency, and can be widely used in practical problems, such as multiclass classification, nonparametric regression, and Poisson regression, among others. We show that the proposed method is selection consistent and $L_2$ consistent under weak regularity conditions. We further demonstrate the superior performance of the proposed method over other existing methods by simulations and real data examples.

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