Impact of class imbalance on convolutional neural network training in multi-class problems
Image classification is the process of assigning an image one or multiple tags that describe its content. To perform the classification, a model must be designed for learning the labels to be assigned to a given image. The assignment is made through a learning process that uses a set of previously l...
- Autores:
-
Ilham, Ahmad
Silva, Jesús
Mercado Caruso, Nohora Nubia
Tapias, Donato
Pineda, Omar
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7258
- Acceso en línea:
- https://hdl.handle.net/11323/7258
https://repositorio.cuc.edu.co/
- Palabra clave:
- Convolutional neural network
Impact of class imbalance
Multi-class problems
- Rights
- closedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Impact of class imbalance on convolutional neural network training in multi-class problems |
| title |
Impact of class imbalance on convolutional neural network training in multi-class problems |
| spellingShingle |
Impact of class imbalance on convolutional neural network training in multi-class problems Convolutional neural network Impact of class imbalance Multi-class problems |
| title_short |
Impact of class imbalance on convolutional neural network training in multi-class problems |
| title_full |
Impact of class imbalance on convolutional neural network training in multi-class problems |
| title_fullStr |
Impact of class imbalance on convolutional neural network training in multi-class problems |
| title_full_unstemmed |
Impact of class imbalance on convolutional neural network training in multi-class problems |
| title_sort |
Impact of class imbalance on convolutional neural network training in multi-class problems |
| dc.creator.fl_str_mv |
Ilham, Ahmad Silva, Jesús Mercado Caruso, Nohora Nubia Tapias, Donato Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Ilham, Ahmad Silva, Jesús Mercado Caruso, Nohora Nubia Tapias, Donato Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Convolutional neural network Impact of class imbalance Multi-class problems |
| topic |
Convolutional neural network Impact of class imbalance Multi-class problems |
| description |
Image classification is the process of assigning an image one or multiple tags that describe its content. To perform the classification, a model must be designed for learning the labels to be assigned to a given image. The assignment is made through a learning process that uses a set of previously labeled training images, which must be large enough to guarantee efficient training. Many approaches have been researched to find optimal solutions to classification problems, however, databases with large amounts of images and the increased processing power of GPUs have made convolutional neural networks (CNNs) the best choice, as they outperform traditional algorithms. This paper presents a systematic analysis aimed at understanding how the issue of class inequality affects the efficiency of a convolutionary neural network trained for a task of image classification, and presents a technique for correcting the overtraining and that the network generalization. |
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2020 |
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2020-11-11T16:45:08Z |
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2020-11-11T16:45:08Z |
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2020 |
| dc.date.embargoEnd.none.fl_str_mv |
2021-05-07 |
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Pre-Publicación |
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http://purl.org/coar/resource_type/c_816b |
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Text |
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Corporación Universidad de la Costa |
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eng |
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Khan, S.H., Hayat, M., Bennamoun, M., Sohel, F.A., Togneri, R.: Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3573–3587 (2017) Kalinin, A.A., Iglovikov, V.I., Rakhlin, A., Shvets, A. A.: Medical image segmentation using deep neural networks with pre-trained encoders. In: Deep Learning Applications, pp. 39–52. Springer, Singapore (2020) Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249–259 (2018) Dong, Q., Zhu, X., Gong, S.: Single-label multi-class image classification by deep logistic regression. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3486–3493, July 2019 Talo, M., Yildirim, O., Baloglu, U.B., Aydin, G., Acharya, U.R.: Convolutional neural networks for multi-class brain disease detection using MRI images. Comput. Med. Imaging Graph. 78, 101673 (2019) Nguyen, T.D., Kasmarik, K.E., Abbass, H.A.: An exact transformation from deep neural networks to multi-class multivariate decision trees. arXiv preprint arXiv:2003.04675 (2020) Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020) Raghu, S., Sriraam, N., Temel, Y., Rao, S.V., Kubben, P.L.: EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Netw. 124, 202–212 (2020) Fidon, L., Li, W., Garcia-Peraza-Herrera, L. C., Ekanayake, J., Kitchen, N., Ourselin, S., Vercauteren, T.: Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks. In: International MICCAI Brainlesion Workshop, pp. 64–76. Springer, Cham, September 2017 Benegui, C., Ionescu, R.T.: Convolutional neural networks for user identification based on motion sensors represented as images. IEEE Access 8(6), 61255–61266 (2020) Talo, M.: Convolutional neural networks for multi-class histopathology image classification. arXiv preprint arXiv:1903.10035 (2019) Shahtalebi, S., Asif, A., Mohammadi, A.: Siamese Neural networks for EEG-based Brain-computer Interfaces. arXiv preprint arXiv:2002.00904 (2020) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Single-shot refinement neural network for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4203–4212 (2018) Jmour, N., Zayen, S., Abdelkrim, A.: Convolutional neural networks for image classification. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), pp. 397–402. IEEE, March 2018 Park, T., Lee, T.: Musical instrument sound classification with deep convolutional neural network using feature fusion approach. arXiv preprint arXiv:1512.07370 (2015) Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten chinese character recognition using googlenet and directional feature maps. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE, August 2015 Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019) Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597 (2016) Jacob, I.J.: Capsule network based biometric recognition system. J. Artif. Intell. 1(02) 83–94 (2019) McDonnell, M.D., Vladusich, T.: Enhanced image classification with a fast-learning shallow convolutional neural network. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE, July 2015 Du, J., Zhai, J.F., Hu, J.S., Zhu, B., Wei, S., Dai, L.R.: Writer adaptive feature extraction based on convolutional neural networks for online handwritten Chinese character recognition. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 841–845. IEEE, August 2015 Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs, pp. 1201–1206, ANT/EDI40 (2019) Wang, H., Ding, S., Wu, D., Zhang, Y., Yang, S.: Smart connected electronic gastroscope system for gastric cancer screening using multi-column convolutional neural networks. Int. J. Prod. Res. 57(21), 6795–6806 (2019) Sharma, N., Jain, V., Mishra, A.: An analysis of convolutional neural networks for image classification. Procedia Comput. Sci. 132, 377–384 (2018) Yim, J., Sohn, K.A.: Enhancing the performance of convolutional neural networks on quality degraded datasets. In: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE, November 2017 Silva, J., Palma, H.H., Núñez, W.N., Ruiz-Lazaro, A., Varela, N.: Neural networks for tea leaf classification. J. Phys: Conf. Ser. 1432(1), 012075 (2020) Zeng, Y., Xu, X., Fang, Y., Zhao, K.: Traffic sign recognition using extreme learning classifier with deep convolutional features. In: The 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015), vol. 9242, pp. 272–280, Suzhou, June 2015 Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649. IEEE, June 2012 Ariza, P., Piñeres, M., Santiago, L., Mercado, N., De la Hoz, A.: Implementation of MOPROSOFT level I and II in software development companies in the colombian caribbean, a commitment to the software product quality region. In: 2014 IEEE Central America and Panama Convention (CONCAPAN XXXIV), pp. 1–5, Panama City (2014). https://ezproxy.cuc.edu.co:2067/10.1109/CONCAPAN.2014.7000402 |
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Ilham, AhmadSilva, JesúsMercado Caruso, Nohora NubiaTapias, DonatoPineda, Omar2020-11-11T16:45:08Z2020-11-11T16:45:08Z20202021-05-072194-5357https://hdl.handle.net/11323/7258Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Image classification is the process of assigning an image one or multiple tags that describe its content. To perform the classification, a model must be designed for learning the labels to be assigned to a given image. The assignment is made through a learning process that uses a set of previously labeled training images, which must be large enough to guarantee efficient training. Many approaches have been researched to find optimal solutions to classification problems, however, databases with large amounts of images and the increased processing power of GPUs have made convolutional neural networks (CNNs) the best choice, as they outperform traditional algorithms. This paper presents a systematic analysis aimed at understanding how the issue of class inequality affects the efficiency of a convolutionary neural network trained for a task of image classification, and presents a technique for correcting the overtraining and that the network generalization.Ilham, Ahmad-will be generated-orcid-0000-0003-0211-2682-600Silva, JesúsMercado Caruso, Nohora Nubia-will be generated-orcid-0000-0001-9261-8331-600Tapias, Donato-will be generated-orcid-0000-0003-3838-4225-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAdvances in Intelligent Systems and Computinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089228091&doi=10.1007%2f978-3-030-51859-2_28&partnerID=40&md5=ac05fd947220584bbc26c8914f14071eConvolutional neural networkImpact of class imbalanceMulti-class problemsImpact of class imbalance on convolutional neural network training in multi-class problemsPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionKhan, S.H., Hayat, M., Bennamoun, M., Sohel, F.A., Togneri, R.: Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3573–3587 (2017)Kalinin, A.A., Iglovikov, V.I., Rakhlin, A., Shvets, A. A.: Medical image segmentation using deep neural networks with pre-trained encoders. In: Deep Learning Applications, pp. 39–52. Springer, Singapore (2020)Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249–259 (2018)Dong, Q., Zhu, X., Gong, S.: Single-label multi-class image classification by deep logistic regression. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3486–3493, July 2019Talo, M., Yildirim, O., Baloglu, U.B., Aydin, G., Acharya, U.R.: Convolutional neural networks for multi-class brain disease detection using MRI images. Comput. Med. Imaging Graph. 78, 101673 (2019)Nguyen, T.D., Kasmarik, K.E., Abbass, H.A.: An exact transformation from deep neural networks to multi-class multivariate decision trees. arXiv preprint arXiv:2003.04675 (2020)Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020)Raghu, S., Sriraam, N., Temel, Y., Rao, S.V., Kubben, P.L.: EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Netw. 124, 202–212 (2020)Fidon, L., Li, W., Garcia-Peraza-Herrera, L. C., Ekanayake, J., Kitchen, N., Ourselin, S., Vercauteren, T.: Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks. In: International MICCAI Brainlesion Workshop, pp. 64–76. Springer, Cham, September 2017Benegui, C., Ionescu, R.T.: Convolutional neural networks for user identification based on motion sensors represented as images. IEEE Access 8(6), 61255–61266 (2020)Talo, M.: Convolutional neural networks for multi-class histopathology image classification. arXiv preprint arXiv:1903.10035 (2019)Shahtalebi, S., Asif, A., Mohammadi, A.: Siamese Neural networks for EEG-based Brain-computer Interfaces. arXiv preprint arXiv:2002.00904 (2020)Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Single-shot refinement neural network for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4203–4212 (2018)Jmour, N., Zayen, S., Abdelkrim, A.: Convolutional neural networks for image classification. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), pp. 397–402. IEEE, March 2018Park, T., Lee, T.: Musical instrument sound classification with deep convolutional neural network using feature fusion approach. arXiv preprint arXiv:1512.07370 (2015)Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten chinese character recognition using googlenet and directional feature maps. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE, August 2015Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597 (2016)Jacob, I.J.: Capsule network based biometric recognition system. J. Artif. Intell. 1(02) 83–94 (2019)McDonnell, M.D., Vladusich, T.: Enhanced image classification with a fast-learning shallow convolutional neural network. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE, July 2015Du, J., Zhai, J.F., Hu, J.S., Zhu, B., Wei, S., Dai, L.R.: Writer adaptive feature extraction based on convolutional neural networks for online handwritten Chinese character recognition. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 841–845. IEEE, August 2015Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs, pp. 1201–1206, ANT/EDI40 (2019)Wang, H., Ding, S., Wu, D., Zhang, Y., Yang, S.: Smart connected electronic gastroscope system for gastric cancer screening using multi-column convolutional neural networks. Int. J. Prod. Res. 57(21), 6795–6806 (2019)Sharma, N., Jain, V., Mishra, A.: An analysis of convolutional neural networks for image classification. Procedia Comput. Sci. 132, 377–384 (2018)Yim, J., Sohn, K.A.: Enhancing the performance of convolutional neural networks on quality degraded datasets. In: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE, November 2017Silva, J., Palma, H.H., Núñez, W.N., Ruiz-Lazaro, A., Varela, N.: Neural networks for tea leaf classification. J. Phys: Conf. Ser. 1432(1), 012075 (2020)Zeng, Y., Xu, X., Fang, Y., Zhao, K.: Traffic sign recognition using extreme learning classifier with deep convolutional features. In: The 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015), vol. 9242, pp. 272–280, Suzhou, June 2015Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649. IEEE, June 2012Ariza, P., Piñeres, M., Santiago, L., Mercado, N., De la Hoz, A.: Implementation of MOPROSOFT level I and II in software development companies in the colombian caribbean, a commitment to the software product quality region. 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