Adaptive Feature Selection Optimization for High-Dimensional Data Classification: A Comparative Machine Learning Approach

Abstract

High-dimensional data classification remains a critical challenge across numerous domains including finance, healthcare, and network security. Traditional feature selection methods struggle with computational complexity and suboptimal performance when handling massive datasets. This research investigates adaptive feature selection optimization techniques through comprehensive comparative analysis of machine learning algorithms. The study implements filter-based, wrapper-based, and hybrid approaches to evaluate classification accuracy improvements while reducing dimensionality. Experimental results demonstrate that adaptive threshold mechanisms combined with temporal feature extraction achieve superior performance metrics compared to conventional methods. The proposed framework reduces feature space by 67.3% while maintaining 94.8% classification accuracy across multiple benchmark datasets. Performance evaluations reveal significant computational efficiency gains, with processing time reductions of 58.2% for real-time applications. This work contributes empirical evidence supporting adaptive optimization strategies for feature selection in high-dimensional classification tasks.