Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm
Shinn‐Ying Ho, Chia‐Cheng Liu, Soundy Liu, Jun-Wen Jou · 2003
The goal of designing an optimal nearest-neighbor classifier is to maximize the classification accuracy while minimizing the sizes of both the reference and feature sets. A novel intelligent genetic algorithm (IGA), which is superior to conventional genetic algorithms (GAs) in solving large parameter optimization problems, is used to effectively achieve this goal. It is shown empirically that the IGA-designed classifier outperforms existing GA-based and non-GA-based classifiers in terms of classification accuracy and the total number of parameters of the reduced sets.