An Empirical Analysis of Data Reduction Techniques for k-NN Classification

This study explores Data Reduction Techniques (DRTs) in the realm of lazy classification algorithms like k-NN, focusing on Prototype Selection (PS) and Prototype Generation (PG) methods. The research provides an in-depth examination of these methodologies, categorizing DRTs into two primary categories: PS and PG, and further dividing them into three sub-categories: condensation methods, edition methods, and hybrid methods. An experimental study compares a total of 20 new and state-of-the-art DRTs across 20 datasets. The objective is to draw performance conclusions within both the primary and subcategories, offering valuable insights into how these techniques enhance the effectiveness and robustness of the k-NN classifier. The paper provides a comprehensive overview of DRTs, clarifying their strategies and relative performances.