TITLE:
Model-Free Feature Screening via Maximal Information Coefficient (MIC) for Ultrahigh-Dimensional Multiclass Classification
AUTHORS:
Tingting Chen, Guangming Deng
KEYWORDS:
Ultrahigh-Dimensional, Feature Screening, Model-Free, Maximal Information Coefficient (MIC), Multiclass Classification
JOURNAL NAME:
Open Journal of Statistics,
Vol.13 No.6,
December
28,
2023
ABSTRACT: It is common for datasets to contain both
categorical and continuous variables. However, many feature screening methods
designed for high-dimensional classification assume that the variables are
continuous. This limits the applicability of
existing methods in handling this complex scenario. To address this issue,
we propose a model-free feature screening approach for ultra-high-dimensional
multi-classification that can handle both categorical and continuous variables.
Our proposed feature screening method utilizes the Maximal Information
Coefficient to assess the predictive power of the variables. By satisfying
certain regularity conditions, we have proven that our screening procedure
possesses the sure screening property and ranking consistency properties. To
validate the effectiveness of our approach, we conduct simulation studies and
provide real data analysis examples to demonstrate its performance in finite
samples. In summary, our proposed method offers a solution for effectively
screening features in ultra-high-dimensional datasets with a mixture of
categorical and continuous covariates.