Traffic sign recognition using scale invariant feature transform and bagging based ensemble

Learning 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.