EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm.

ABSTRACT: Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the perf

SUBMITTER: Huang L 

PROVIDER: S-EPMC9322764 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature