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oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/165842026-10-18T20:36:28Zopenairedrivercom_ebyu_10000col_ebyu_10001

Traffic Sign Recognition Using Scale Invariant Feature Transform and Bagging Based EnsembleAydin, YildizOzyer, Gulsah TumukluOzdemir, DurmusScale invariant feature transformTraffic Sign RecognitionImbalanced DatasetEngineering, Electrical & ElectronicEngineeringLearning from imbalanced data sets is an important problem frequently encountered in the application of classification problems. Instances of this type of problem is usually labeled with the label of class majority and minority class instances will be ignored. In this study, an ensemble based method is proposed for problems of imbalanced data set. The results obtained were compared to alternative traditional classifier (support vector machine (svm) and k nearest neighbor classifier (knn)). Bagging -based ensemble classifier eliminates the problem of bias. Thus minority class are classified correctly and improving the application performance is provided.IEEE2016Conference Objecthttps://rdlopen.rawdatalibrary.net/r/ebyu/16584978-1-5090-1679-2tr2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU)info:eu-repo/semantics/openAccessinfo:eu-repo/semantics/conferenceObjecthttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/16584