Comparative analysis of ensemble learning methods for signal classification; [Sinyal siniflandirmasi için topluluk öǧrenmesi yöntemlerinin karşilaştirmali analizi]

Simple item page
dc.contributor.authorYildirim P.
dc.contributor.authorBirant K.U.
dc.contributor.authorRadevski V.
dc.contributor.authorKut A.
dc.contributor.authorBirant D.
dc.date.accessioned2024-07-22T08:09:44Z
dc.date.available2024-07-22T08:09:44Z
dc.date.issued2018
dc.description.abstractIn 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.
dc.identifier.DOI-ID10.1109/SIU.2018.8404601
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/14947
dc.language.isoTurkish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectArtificial intelligence
dc.subjectClassification (of information)
dc.subjectDecision trees
dc.subjectLearning systems
dc.subjectNearest neighbor search
dc.subjectSignal processing
dc.subjectClassification accuracy
dc.subjectClassification algorithm
dc.subjectClassification performance
dc.subjectComparative analysis
dc.subjectEnsemble learning
dc.subjectMachine learning approaches
dc.subjectRandom forest algorithm
dc.subjectSignal classification
dc.subjectLearning algorithms
dc.titleComparative analysis of ensemble learning methods for signal classification; [Sinyal siniflandirmasi için topluluk öǧrenmesi yöntemlerinin karşilaştirmali analizi]
dc.typeConference paper

Files

Collections

Scopus Koleksiyonu