An Evolvable?Clustering?Based Algorithm to Learn Distance Function for Supervised Environment
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.
Keywords: distance function learning, evolutionary
algorithm, clustering algorithm, nearest neighbor
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