Traffic Sign Recognition System for Imbalanced Dataset
Abstract—In classification problem, the most important factor is training dataset which is effect accuracy rate of classification. However, we encounter with imbalanced data set in real-world applications. In this dataset, the number of images in some classes is rather less than the number of images in other classes. So estimation of classification is tent to majority class and minority classes will be ignored. In this study, an ensemble based method is proposed for increasing accuracy rate of minority class. The results obtained are compared with traditional classifiers (support vector machine (SVM) and k nearest neighbor classifier (KNN)). Bagging based ensemble classifier takes out the issue of inclination toward classifying minority class. As a result, the accuracy of our method result is higher and more efficiency than the other two traditional classifiers.
Index Terms—Scale invariant feature transform, speed-up robust features, bagging based ensemble, imbalanced dataset, traffic sign recognition.
Cite: Yildiz Aydin, Durmus Ozdemir, Gulsah Tumuklu Ozyer, "Traffic Sign Recognition System for Imbalanced Dataset," Journal of Computers vol. 12, no. 6, pp. 543-549, 2017.
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