GA-Facilitated Classifier Optimization with Varying Similarity Measures

Genetic algorithms are powerful tools for k-nearest neigh-bors classification. Traditional knn classifiers employ Eu-clidian distance to assess neighbor similarity, though other measures may also be used. GAs can search for optimal lin-ear weights of features to improve knn performance using both Euclidian distance and cosine similarity. GAs also op-timize additive feature offsets in search of an optimal point of reference for assessing angular similarity using the cosine measure. This poster explores weight and offset optimiza-tion for knn with varying similarity measures, including Eu