Comparison of Classification Algorithms on Optical Burst Switching Network Data

Abstract

Classification, a model of data mining, is simply to distribute data in defined groups in a data set. Classification algorithms develop a learning model using the training set and then try to classify the data that is not class specific. Therefore, the relationship between the data can be examined more easily. In this study, multilayer perceptron, support vector machines, k-nearset neighborhood, naive bayes, kstar, decision table, random forest classification algorithms performans are evaluated by using optical burst switching algorithms. When data sets are classified by using classification algorithms, accuracy values, which are the ratio of accurate estimates to all estimates during classification, are used as a criterion for evaluating the performance of algorithms. © 2019 IEEE.