An Evolvable-Clustering-Based Algorithm to Learn Distance Function for Supervised Environment
Zeinab Khorshidpour, Sattar Hashemi, Ali Hamzeh · 2010
This paper introduces a novel weight‐based approach to learn distance function to find the weights that induce a clustering by meeting best objective function. Our method combines clustering and evolutionary algorithms for learning weights of distance function. Evolutionary algorithms, are proved to be good techniques for finding optimal solutions in a large solution space and to be stable in the presence of noise. Experiments with UCI datasets show that employing EA to learn the distance function improves the accuracy of the popular nearest neighbor classifier.