An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma.

ABSTRACT: Feature selection plays a crucial role in classification tasks as part of the data preprocessing process. Effective feature selection can improve the robustness and interpretability of learning algorithms, and accelerate model learning. However, traditional statistical methods for feature selection are no longer practical in the context of high-dimensional data due to the computationally complex. Ensemble learning, a prominent learning method in machine learning, has demonstrated exceptional performance, particularly in classification problems. To address the issue, we propose a three-stage feature selection algorithm framework for high-dimensional data based on ensemble learning (EFS-GINI). Firstly, highly linearly correlated features are eliminated using the Spearman coefficient. Then, a

SUBMITTER: Xin Z 

PROVIDER: S-EPMC10773758 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature