Keywords and phrases: Keywords and phrases: imbalanced data, traffic sign recognition, oversampling, classification, convolutional neural networks.
Received: October 3, 2022; Accepted: November 28, 2022; Published: December 14, 2022
How to cite this article: Idi Boubacar Sani, Ibrahim Sidi Zakari and Moctar Mossi Idrissa, Imbalanced multiclass traffic sign images classification based on minority oversampling and convolutional neural networks, Advances and Applications in Statistics 83 (2022), 121-132. http://dx.doi.org/10.17654/0972361722089
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References:
[1] D. Ramyachitra and P. Manikandan, Imbalanced dataset classification and solutions: a review, International Journal of Computing and Business Research (IJCBR) 5(4) (2014), 1-29. [2] K. Simonyan and A. Zisserman, Very deep convolutional networks for large scale image recognition 2015. arXiv preprint, https://arxiv.org/abs/1409.1556. [3] A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds., Curran Associates, Inc., 2012, pp. 1097-1105. [4] Y. Yu, J. Li, C. Wen, H. Guan, H. Luo and C. Wang, Bag-of visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data, ISPRS Journal of Photogrammetry and Remote Sensing 113 (2016), 106-123. [5] K. Polat, Similarity-based attribute weighting methods via clustering algorithms in the classification of imbalanced medical datasets, Neural Comput. Appl. 30 (2018), 987-1013. [6] W. Han, Z. Huang, S. Li and Y. Jia, Distribution-sensitive unbalanced data oversampling method for medical diagnosis, J. Med. Syst. 43 (2019), 39. [7] H. He and E. A. Garcia, Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering 21(9) (2009), 1263-1284. [8] G. Menardi and N. Torelli, Training and assessing classification rules with imbalanced data, Data Mining and Knowledge Discovery 28(1) (2014), 92-122. [9] E. C. Orenstein, O. Beijbom, E. E. Peacock and H. M. Sosik, WHOI-plankton- A large scale fine grained visual recognition benchmark dataset for plankton classification, 2015. arXiv preprint, https://arxiv.org/abs/1510.00745. [10] H. Lee, M. Park and J. Kim, Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning, 2016 IEEE International Conference on Image Processing (ICIP) 2016, pp. 3713-3717. [11] Z. Fan, Y. Wu, C. Zhou, X. Zhang and Z. Tao, Class-imbalanced voice pathology detection and classification using fuzzy cluster oversampling method, Applied Sciences 11(8) (2021), 3450. [12] Y. Wu, Z. Li, Y. Chen, K. Nai and J. Yuan, Real-time traffic sign detection and classification towards real traffic scene, Multimedia Tools and Applications 79(25) (2020), 18201-18219. [13] H. Kaur, H. S. Pannu and A. K. Malhi, A systematic review on imbalanced data challenges in machine learning: applications and solutions, ACM Computing Surveys (CSUR) 52(4) (2019), 1-36. [14] H. Jegierski and S. Saganowski, An “outside the box” solution for imbalanced data classification, IEEE Access 8 (2020), 125191-125209. [15] Chinese Traffic Sign Recognition Database, Available online: http://www.nlpr.ia.ac.cn/pal/trafficdata/recognition.html (accessed on 03/07/2022). [16] I. B. Sani, I. S. Zakari, M. M. Idrissa and D. Abdourahimoun, Machine learning based classification of traffic signs images from a robot-car, IEEE Access (proceedings of the IEEE Multi-conference on Natural and Engineering Sciences for Sahel’s Sustainable Development (MNE3SD)), 2022, pp. 1-6. [17] B. Kumar, 10 Techniques to deal with Imbalanced Classes in Machine Learning, Available online: https://www.analyticsvidhya.com/blog/2020/07/10-techniques-to-deal-with-class-imbalance-in-machine-learning/ (accessed on 02/08/2022).
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