Supervised ML Algorithms in the High Dimensional Applications for Dimension Reduction

Abstract

We steered comparative analysis of manifold supervised dimension reduction methods by assimilating customary multiobjective standard metrics and validated the comparative efficacy of supervised learning algorithms in reliance on data and sample complexity. The question of sample and data intricacy is deliberated in dependence on automating selection and user-purposed instances. Different dimension reduction techniques are responsive to different scales of measurement and supervision of learning is also discussed comprehensively. In line with the prospects, each technique validated diverse competence for different datasets and there was no mode to gauge the general ranking of methods trustily available. We especially engrossed the classifier ranking and concocted a system erected on weighted average rank called weighted mean rank risk adjusted model (WMRRAM) for consensus ranking of supervised learning classifier algorithms.