Study of different KNN aldorithm versions

Abstract

K-Nearest Neighbor algorithm has been proven to be a simple and effective method for classification problems in machine learning. This Final Degree Project is based on the investigation of the KNN (K-Nearest Neighbors) algorithm introducing some changes that improve said algorithm: fuzzy logic, fuzzy intervals and evolutionary algorithms. First, a model using fuzzy logic and a model with fuzzy intervals are created, which improve to a certain extent the accuracy of the original model (KNN). Next, the evolutionary algorithm is used to try to improve the performance of the model further. The problem is the time required for the convergence of this last algorithm. Therefore, it is intended to make a comparison between all these models and see the difference between them in both performance and time.