A genetic algorithm for learning weights in a similarity function

摘要

One large problem when employing a similarity function to measure the similarities between new and prior cases is to determine the weights of t he features. This paper proposes a new method of learning weights using a genetic algorithm based on the similarity information of given examples. This method is suitable for both linear and nonlinear similarity functions. Our experimental results show the computational efficiency of the proposed approach.