Optimizing Feature Selection in Big Data Analytics with Adaptive Particle Swarm for Enhanced Model Precision

Feature selection (FS), in the context of Big Data analytics, is essential for improving the effectiveness and precision of machine learning models by removing unnecessary and redundant information. Customary FS procedures frequently find it challenging to deal with Large Information's high dimensionality and volume. The Versatile Molecule Multitude Advancement (APSO) technique for FS in large information examination is introduced in this review. The proposed APSO, rather than conventional PSO calculations, continually changes its boundaries as per the properties of the information, ensuring faster combination and improved arrangement quality. Various benchmark Huge Information datasets were utilized for broad trial and error, and the results were contrasted with state-of-the-art FS techniques. As far as figuring effectiveness and arrangement quality, the APSO performed better. Furthermore, the attributes that APSO decided to consolidate with AI models brought about diminished model intricacy and expanded expectation precision. This study features APSO's commitment as a versatile and dependable FS method for Huge Information investigation, overcoming any issues between the requirements of contemporary information driven applications and heuristic improvement.