期刊论文

Xianlai Chen

英文

Alexandria Engineering Journal

1110-0168

2023

66

993-999

10.1016/j.aej.2022.10.069

This work is supported by the National Science Foundation of Hunan Province (Grants No.: 2022JJ30747, 2021JJ30082), the Project of Intelligent Management Software for Multimodal Medical Big Data for New Generation Information Technology, Ministry of Industry and Information Technology of People’s Republic of China (Grant No.: TC210804V), General Project of Education Department of Hunan Province (Grant No.: 20C0373).

本校为其他机构

Radiomics is characterized by high-dimension and high redundancy. The existing Lasso-based feature selection does not consider features that are weakly correlated with the clas-sification results, which will have a certain impact on the quality of feature subset. A multi-level feature selection algorithm based on Lasso coefficient threshold (Coe-Thr-Lasso) was proposed. Firstly, t-test and variance were used to remove the features that had little correlation with the classification results. Secondly, the proposed algorithm was used to remove features with redun-dancy and weak correlation of cl...