Prior knowledge evaluation and emphasis sampling-based evolutionary algorithm for high-dimensional medical data feature selection

Abstract

Handling high-dimensional medical data presents a significant challenge. Numerous irrelevant and redundant features impede the construction of high-precision models. Traditional Feature Selection (FS) algorithms often fail to address the nonlinear and combinatorial relationships inherent in high-dimensional medical features. To address these issues, we propose the Prior Knowledge Evaluation Strategy (PES) and Emphasis Sampling Strategy (ESS) based Harris Hawks Optimization (PSHHO) algorithm. The PES strategy stores the historical optimal solutions in an archive, effectively eliminating low-quality and invalid solutions, thereby reducing evaluation time. The ESS mechanism enables the Harris Hawk Optimization algorithm to fully utilize the valuable information embedded in the optimal solutions, enhancing its optimization performance through equidistant sampling for local exploitation. Additionally, a V-shaped transfer function is employed to convert the algorithm into its binary version, BPSHHO, for FS in high-dimensional medical data. The experimental results demonstrate that the PSHHO algorithm significantly outperforms the compared algorithms on the CEC benchmark test set, achieving the global best results on 17 out of 30 test functions. It exhibits excellent performance on medical datasets with dimensions exceeding 5000. Compared to other binary algorithms, BPSHHO achieves the highest classification accuracy with the fewest features. On 9 high-dimensional medical datasets, it achieved top accuracy in 8 cases using classifiers constructed with no more than 15 features. This highlights its effectiveness as a FS method for high-dimensional medical data.