Yildirim P.Birant K.U.Radevski V.Kut A.Birant D.2024-07-222024-07-222018http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/14947In recent years, the machine learning algorithms commenced to be used widely in signal classification area as well as many other areas. Ensemble learning has become one of the most popular Machine Learning approaches due to the high classification performance it provides. In this study, the application of four fundamental ensemble learning methods (Bagging, Boosting, Stacking, and Voting) with five different classification algorithms (Neural Network, Support Vector Machines, k-Nearest Neighbor, Naive Bayes, and C4.5) with the most optimal parameter values on signal datasets is presented. In the experimental studies, ensemble learning methods were applied on 14 different signal datasets and the results were compared in terms of classification accuracy rates. According to the results, the best classification performance was obtained with the Random Forest algorithm which is a Bagging based method. © 2018 IEEE.TurkishArtificial intelligenceClassification (of information)Decision treesLearning systemsNearest neighbor searchSignal processingClassification accuracyClassification algorithmClassification performanceComparative analysisEnsemble learningMachine learning approachesRandom forest algorithmSignal classificationLearning algorithmsConference paper10.1109/SIU.2018.8404601