Comparative Analysis of Machine Learning Algorithms for Binary Classification SARISH

Abstract

Machine learning algorithms are applied in all domains to achieve classification tasks. Machine Learning is applicable to several real life problems. Aim of this paper is highly accurate predictions in test data sets using machine learning methods and comparison of these methods to select appropriate method for a particular data set for binary classifications. Three machine learning methods Artificial Neural Network (Multi-Layer Perceptron with Back Propagation Neural Network), Support Vector Machine and K-Nearest Neighbor are used in this research work. The data sets are taken from UCI website. A comparative study is carried out to evaluate the performance of the classifiers using statistical measures e.g. accuracy, specificity and sensitivity. These results are also compared with previous studies. Experimental outcomes show that the Artificial Neural Network method provides better performance, and it is strongly suggested that the Multi-Layer Perceptron with Back Propagation Neural Network method is reasonably operational for the task of binary classification followed by Support Vector Machine and K-Nearest Neighbor.