Work

Structural feature selection via weighted sparse regression with mutual information

Year: 2025

Type: article

Abstract: Feature selection in high-dimensional data is an important part of the data mining process and is widely used in bioinformatics, statistics and image processing fields. Successfully selecting informative features can significantly improve learning accuracy and improve result comprehensibility. However, it is a challenging problem to select features accurately and efficiently from high-dimensional data. In this paper, we propose a Weighted Sparse Regression with Mutual Information (WSRMI) for selecting structural features. Differing from traditional sparse feature selection models that focus solely on either feature correlations or feature importance, the proposed model integrates both aspects through a mutual-information-based weighting mechanism. The proposed model can be effectively applied to regression and binary classification tasks, making it more general and practical for real-world applications. The proposed model is statistically compared with several existing classical models over randomly generated classification and benchmark datasets. Experimental results show that the proposed model is more effective at selecting the informative features with a superior prediction performance than the comparative ones.

Source: Journal Of Big Data

DOI: 10.1186/s40537-025-01344-x

Authors: Yadi Wang, Yulin Xie, Sufang Zhou, Bingbing Jiang, Hangjun Che

Institutions: Henan University, Chongqing University of Posts and Telecommunications, Hangzhou Normal University, Southwest University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,290

Funder: National Natural Science Foundation of China