Reducing the Number of Calculations in k-nn by Class Representatives AtB Voting

The problem of clustering and classification is a key task in the field of artificial intelligence. Due to the increasing amounts of data in the digital world, existing solutions must be quickly adapted to them. Unfortunately, some solutions have quite big limitations. An example of what is k-nearest neighbors (k-nn). In this paper, we propose two improvements, which can be used for increasing the accuracy of used tools and decrease the operating time. Described modification focus on introducing class representatives and voting mechanism among the best data. Voting is carried out on samples that have achieved some degree of being the best, such as above the average distance among all of them using the Euclidean metric. The proposed solutions are tested and analyzed against the original version of k-nn.