A multi-objective evolutionary algorithm with decomposition and the information feedback for high-dimensional medical data

Abstract

High-dimensional medical data often leads to a phenomenon known as the ”curse of dimensionality,” which causes additional memory and high training costs, as well as degrading the generalization capacity of learning algorithms. To address this issue, a multi-objective evolutionary algorithm that integrates decomposition and the information feedback model (IFMMOEAD) is proposed for high-dimensional medical data. This algorithm not only considers the number of selected features, but also classification accuracy and correlation measures of features when feature dimensionality reduction is executed. The property of IFMMOEAD is first verified by standard benchmarks DTLZ1–DTLZ7. Then, it is used to develop machine learning algorithms for thirty-five high-dimensional cancer gene expression data sets, showing excellent potential for high-dimensional medical machine learning. Finally, the IFMMOEAD is applied to empirical clinical data of multiple myeloma, significantly outperforming existing algorithms in terms of normalized mutual information and adjusted rand index metrics. We suggest that this algorithm could be implemented in medical information systems as a promising technique for high-dimensional medical problems.