Grey Wolf Optimization and PCA-Based Hybrid Method for Dimensionality Reduction

High-dimensional datasets across various domains, including healthcare, industry, and social media, present challenges such as overfitting, computational expense, and diminished interpretability. Identifying the most relevant variables is crucial for addressing these issues through feature selection. In this proposed method, in the first step Grey Wolf Optimizer (GWO) algorithm is used to select the best feature the second step, the selected features are reduced by using the PCA algorithm. This investigation assessed GWO and PCA across ten high-dimensional datasets, employing selected features to train K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) classifiers. The proposed method significantly decreased feature counts by factors ranging from 10 to 100, all while preserving or enhancing accuracy. The performance of KNN was frequently enhanced when utilizing features selected through the proposed method. In comparison to PCA, ReliefF, mRMR, Chi-squared, SIFS, ATFS, EmPo, and FSM, the proposed GWO demonstrated enhanced performance; it independently identified optimal subset sizes, thereby increasing its applicability for high-dimensional scenarios in practical settings.