Hybridization of K Nearest Neighbors Classifier with Cuckoo Search Algorithm

Artificial intelligence methods are one of the most used algorithms in big data and Internet of Things solutions. Therefore, a very important aspect is to create new algorithms and improve existing ones. In this paper, the proposition of a hybrid method for classifying elements in certain datasets is presented. The proposed method joins properties of K Nearest Neighbors Algorithm (a classifier) and Cuckoo Search Algorithm (a heuristic algorithm). The proposed model was presented, tested, and discussed on a selected dataset of Iris Flowers. The effectiveness of the method is compared for different variants of input parameters to show the efficiency of the proposition.