بهبود کارایی شبکه عصبی کانولووشنال با استفاده از تابع ضرر وزن‌دار افزایشی برای مقابله با نامتوازنی دسته‌ای

[1]           M. Khosravi, H. Shirazi, K. Dadeshtabar Ahmadi, and S. A. Hashemi Golpayegani, "Rumour detection in social networks based on the analysis of the frequency pattern of vertices in the step-by-step propagation subgraphs," (in Persian), Electronic and Cyber ​​Defense, vol. 10, no. 3, pp. 93-105, 2022

 [2]          J. M. Johnson and T. M. Khoshgoftaar, "Survey on deep learning with class imbalance," Journal of Big Data, vol. 6, no. 1, p. 27, 2019/03/19 2019, doi: 10.1186/s40537-019-0192-5.

[3]           G. Aguiar, B. Krawczyk, and A. Cano, "A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework," 2022. [Online]. Available: http://arXiv.org/abs/.

[4]           C. Huang, Y. Li, C. C. Loy, and X. Tang, "Learning Deep Representation for Imbalanced Classification," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June 2016 2016, pp. 5375-5384, doi: 10.1109/CVPR.2016.580.

[5]           W. Zhang, X. Li, X.-D. Jia, H. Ma, Z. Luo, and X. Li, "Machinery fault diagnosis with imbalanced data using deep generative adversarial networks," Measurement, vol. 152, p. 107377, 2020/02/01/ 2020, doi: https://doi.org/10.1016/j.measurement.2019.107377.

[6]           Q. Dong, S. Gong, and X. Zhu, "Imbalanced Deep Learning by Minority Class Incremental Rectification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 6, pp. 1367–1381, 2019, doi: 10.1109/tpami.2018.2832629.

[7]           R. Anand, K. G. Mehrotra, C. K. Mohan, and S. Ranka, "An improved algorithm for neural network classification of imbalanced training sets," IEEE Transactions on Neural Networks, vol. 4, no. 6, pp. 962-969, 1993, doi: 10.1109/72.286891.

[8]           D. Masko and P. Hensman, "The Impact of Imbalanced Training Data for Convolutional Neural Networks," 2015.

[9]           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), pp. 3713-3717, 2016.

[10]         S. Pouyanfar et al., "Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification," in 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 10-12 April 2018 2018, pp. 112-117, doi: 10.1109/MIPR.2018.00027.

[11]         M. Havaei et al., "Brain tumor segmentation with Deep Neural Networks," Medical Image Analysis, vol. 35, pp. 18-31, 2017/01/01/ 2017, doi: https://doi.org/10.1016/j.media.2016.05.004.

[12]         M. Buda, A. Maki, and M. A. Mazurowski, "A systematic study of the class imbalance problem in convolutional neural networks," Neural Networks, vol. 106, pp. 249-259, 2018/10/01/ 2018, doi: https://doi.org/10.1016/j.neunet.2018.07.011.

[13]         S. Wang, W. Liu, J. Wu, L. Cao, Q. Meng, and P. J. Kennedy, "Training deep neural networks on imbalanced data sets," ed. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016, pp. 4368-4374.

[14]         T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, "Focal Loss for Dense Object Detection," in 2017 IEEE International Conference on Computer Vision (ICCV), 22-29 Oct. 2017 2017, pp. 2999-3007, doi: 10.1109/ICCV.2017.324.

[15]         H. Wang, Z. Cui, Y. Chen, M. Avidan, A. B. Abdallah, and A. Kronzer, "Predicting Hospital Readmission via Cost-Sensitive Deep Learning," IEEE/ACM Transactions on Computational Biology and Bioinformatics, Article vol. 15, no. 6, pp. 1968-1978, 11/1 2018, doi: 10.1109/TCBB.2018.2827029.

[16]         S. H. Khan, M. Hayat, M. Bennamoun, F. A. Sohel, and R. Togneri, "Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data," IEEE Trans. Neural Networks Learn. Syst., vol. 29, no. 8, pp. 3573-3587, / 2018, doi: 10.1109/TNNLS.2017.2732482.

[17]         C. Zhang, K. C. Tan, and R. Ren, "Training cost-sensitive Deep Belief Networks on imbalance data problems," in 2016 International Joint Conference on Neural Networks (IJCNN), 24-29 July 2016 2016, pp. 4362-4367, doi: 10.1109/IJCNN.2016.7727769.

[18]         Y. Zhang, L. Shuai, Y. Ren, and H. Chen, "Image classification with category centers in class imbalance situation," in 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), 18-20 May 2018 2018, pp. 359-363, doi: 10.1109/YAC.2018.8406400.

[19]         W. Ding, D. Huang, Z. Chen, X. Yu, and W. Lin, "Facial action recognition using very deep networks for highly imbalanced class distribution," in 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 12-15 Dec. 2017 2017, pp. 1368-1372, doi: 10.1109/APSIPA.2017.8282246.

[20]         S. Ando and C. Y. Huang, "Deep Over-sampling Framework for Classifying Imbalanced Data," in Machine Learning and Knowledge Discovery in Databases, Cham, M. Ceci, J. Hollmén, L. Todorovski, C. Vens, and S. Džeroski, Eds., 2017// 2017: Springer International Publishing, pp. 770-785.

[21]         A. Y. A. Saeed and A. E. B. Alawi, "Covid-19 Diagnosis Model Using Deep Learning with Focal Loss Technique," in 2021 International Congress of Advanced Technology and Engineering (ICOTEN), 4-5 July 2021 2021, pp. 1-4, doi: 10.1109/ICOTEN52080.2021.9493477.

[22]         A. Taherkhani, G. Cosma, and T. M. McGinnity, "AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning," Neurocomputing, vol. 404, pp. 351-366, 2020/09/03/ 2020, doi: https://doi.org/10.1016/j.neucom.2020.03.064.

[23]         T. J. Hastie, S. Rosset, J. Zhu, and H. Zou, "Multi-class AdaBoostB " Statistics and Its Interface, vol. 2, pp. 349-360, 2009.

[24]         O. EL ZEIN, M. M. SOLIMAN, A. ELKHOLY, and N. I. GHALI, "MULTI-CLASSIFICATION MODEL FOR COVID-19 PREDICTION USING IMBALANCED X-RAY DATASET BASED ON TRANSFER LEARNING AND CLASS WEIGHTING-SMOTE METHOD," Journal of Theoretical and Applied Information Technology, vol. 100, no. 5, 2022.

[25]         م. خالوئی, م. فخردانش, and م. سبک رو, "تشخیص و مکان‌یابی رویداد‌های رایج و نادر در ویدیو با بکارگیری شبکه تخاصمی مولد," (in persian), مجله علمی رایانش نرم و فناوری اطلاعات, vol. 8, no. 3, pp. 40-51, 2019. [Online]. Available: http://jscit.nit.ac.ir/article_93041.html

http://jscit.nit.ac.ir/article_93041_98448d599e92c5238380a9006d717889.pdf.

[26]         A. Choromanska, M. Henaff, M. Mathieu, G. B. Arous, and Y. LeCun, "The loss surfaces of multilayer networks," Journal of Machine Learning Research, Conference article vol. 38, pp. 192-204, 2015. [Online]. Available: http://www.scopus.com/inward/record.url?scp=84954310140&partnerID=8YFLogxK

http://www.scopus.com/inward/citedby.url?scp=84954310140&partnerID=8YFLogxK.

[27]         K. Kawaguchi, Deep Learning without Poor Local Minima, S. a. D. Lee and  and U. L. , I. Guyon , R. Garnett, eds.: Curran Associates, Inc., 2016. [Online]. Available: https://proceedings.neurips.cc/paper/2016/file/f2fc990265c712c49d51a18a32b39f0c-Paper.pdf.

[28]         B. Guo, S. Chen, Z. Hong, and G. Xu, "Pattern Recognition and Analysis: Neural Network using Weighted Cross Entropy," Journal of Physics: Conference Series, vol. 2218, no. 1, p. 012043, 2022/03/01 2022, doi: 10.1088/1742-6596/2218/1/012043.

[29]         Z. Zhou, H. Huang, and B. Fang, "Application of weighted cross-entropy loss function in intrusion detection," Journal of Computer and Communications, vol. 9, no. 11, pp. 1-21, 2021.

[30]         P. Shamsolmoali, M. Zareapoor, L. Shen, A. H. Sadka, and J. Yang, "Imbalanced data learning by minority class augmentation using capsule adversarial networks," Neurocomputing, 2020/07/28/ 2020, doi: https://doi.org/10.1016/j.neucom.2020.01.119.

[31]         J. R. Kwapisz, G. M. Weiss, and S. A. Moore, "Activity Recognition Using Cell Phone Accelerometers," SIGKDD Explor. Newsl., vol. 12, no. 2, pp. 74–82, 2011, doi: 10.1145/1964897.1964918.

[32]         A. Krizhevsky, "Learning Multiple Layers of Features from Tiny Images," University of Toronto, 05/08 2012.

[33]         N. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," J. Artif. Intell. Res. (JAIR), vol. 16, pp. 321-357, 01/01 2002, doi: 10.1613/jair.953.

[34]        J. H. Joloudari, A. Marefat, M. A. Nematollahi, S. S. Oyelere, and S. Hussain, "Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks," Applied Sciences, vol. 13, no. 6, doi: 10.3390/app13064006.