Multi-Label Feature Selection for High-Dimensional Biological Data via Global Relevance and Redundancy Optimization Based on JS Divergence

In recent years, multi-label feature selection has been widely used in fields such as bioinformatics, information retrieval, and multimedia annotation. Most of the previous multi-label feature selection methods are directly transformed from the traditional single-label feature selection methods, or they cannot make full use of label information. As a result, the selected feature subset involves features that are redundant or irrelevant to label information. Moreover, most algorithms do not use discretization when processing continuous data sets, so they cannot effectively eliminate the interference of abnormal data. In order to solve these problems, based on the GRRO (Global Relevance and Redundancy Optimization) algorithm, the JS divergence is used to better measure the overall similarity between two distributions and introduces data discretization preprocessing operations for continuous data sets. The experimental results after experimental verification on ten typical high-dimensional biological data sets show that the GRRO-JS algorithm is superior to the traditional multi-label feature selection method and the GRRO algorithm in terms of accuracy and efficiency and has high practical value.