A Comparative Analysis of K-NN and ANN Techniques in Machine Learning

Abstract

Different machine learning algorithms have been applied in various domains and have yielded good results. The application of a preferred technique to a named field is determined by the type of datasets and target goal in question. Although some researches have shown different techniques resulting to the same prediction result. However, in this study, a critical analysis of the application of k-Nearest Neighbour (k-NN) and Artificial Neural Network (ANN) has been carried out. This comparative analysis was done using the same datasets (English Premiership League) on this same platform (Rapid Miner). K-NN classification showed a prediction success of 53.33% while that of ANN was 70%. This proved that ANN is a better technique than k-NN for a polynomial label.