{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:22Z","timestamp":1750309522203,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":84,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"UK Government's Department for Science, Innovation and Technology (DSIT)"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,17]]},"DOI":"10.1145\/3704137.3704193","type":"proceedings-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T09:32:11Z","timestamp":1740994331000},"page":"231-237","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Class-Imbalanced Learning by Bayesian Optimisation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1835-9225","authenticated-orcid":false,"given":"Tameem","family":"Adel","sequence":"first","affiliation":[{"name":"Data Science Dept., National Physical Laboratory (NPL), Cambridge, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"T. Alam K. Shaukat I. Hameed S. Luo M. Sarwar S. Shabbir J. Li and M. Khushi. 2020. An investigation of credit card default prediction in the imbalanced datasets. IEEE Access 8 (2020).","DOI":"10.1109\/ACCESS.2020.3033784"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"A. Ali-Gombe and E. Elyan. 2019. MFC-GAN: Class-imbalanced dataset classification using multiple fake class generative adversarial network. Neurocomputing 361 (2019) 212\u2013221.","DOI":"10.1016\/j.neucom.2019.06.043"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"T. Almeida J. Gomez and A. Yamakami. 2011. Contributions to the study of SMS spam filtering: New collection and results. ACM Symposium on Document Engineering (2011).","DOI":"10.1145\/2034691.2034742"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"R. Anand K. Mehrotra C. Mohan and S. Ranka. 1993. An improved algorithm for neural network classification of imbalanced training sets. IEEE Transactions on Neural Networks 4 6 (1993) 962\u2013969.","DOI":"10.1109\/72.286891"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"crossref","unstructured":"S. Ando and C. Huang. 2017. Deep over-sampling framework for classifying imbalanced data. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD) (2017) 18\u201322.","DOI":"10.1007\/978-3-319-71249-9_46"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"S. Awan M. Bennamoun F. Sohel F. Sanfilippo and G. Dwivedi. 2021. Imputation of missing data with class imbalance using conditional generative adversarial networks. Neurocomputing 453 (2021) 164\u2013171.","DOI":"10.1016\/j.neucom.2021.04.010"},{"key":"e_1_3_3_2_8_2","unstructured":"O. Bachem M. Lucic and A. Krause. 2015. Coresets for nonparametric estimation \u2013 the case of DP-means. International Conference on Machine Learning (ICML) (2015)."},{"key":"e_1_3_3_2_9_2","unstructured":"A. Bansal M. Goldblum V. Cherepanova A. Schwarzschild C. Bruss and T. Goldstein. 2021. MetaBalance: High-performance neural networks for class-imbalanced data. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2106.09643 (2021)."},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"S. Bengio. 2015. Sharing representations for long tail computer vision problems. International Conference on Multimodal Interaction (ICMI) (2015).","DOI":"10.1145\/2818346.2818348"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"J. Brabec T. Komarek V. Franc and L. Machlica. 2020. On model evaluation under non-constant class imbalance. International Conference on Computational Science (ICCS) (2020) 74\u201387.","DOI":"10.1007\/978-3-030-50423-6_6"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"P. Branco L. Torgo and R. Ribeiro. 2016. A survey of predictive modeling on imbalanced domains. ACM computing surveys (CSUR) 49 2 (2016) 1\u201350.","DOI":"10.1145\/2907070"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","unstructured":"M. Buda A. Maki and M. Mazurowski. 2018. A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks 106 (2018) 249\u2013259.","DOI":"10.1016\/j.neunet.2018.07.011"},{"key":"e_1_3_3_2_14_2","unstructured":"K. Cao C. Wei A. Gaidon N. Arechiga and T. Ma. 2019. Learning imbalanced datasets with label-distribution-aware margin loss. Advances in Neural Information Processing Systems (NeurIPS) (2019)."},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"N. Chawla K. Bowyer L. Hall and W. Kegelmeyer. 2002. SMOTE: Synthetic minority over-sampling technique. Journal of artificial intelligence research (JAIR) 16 (2002) 321\u2013357.","DOI":"10.1613\/jair.953"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"D. Cieslak and N. Chawla. 2008. Start globally optimize locally predict globally: Improving performance on imbalanced data. IEEE International Conference on Data Mining (ICDM) (2008) 143\u2013152.","DOI":"10.1109\/ICDM.2008.87"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Y. Cui M. Jia T. Lin Y. Song and S. Belongie. 2019. Class-balanced loss based on effective number of samples. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019).","DOI":"10.1109\/CVPR.2019.00949"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"A. dal Pazzolo O. Caelen R. Johnson and G. Bontempi. 2015. Calibrating probability with undersampling for unbalanced classification. EEE Symposium Series on Computational Intelligence (2015) 159\u2013166.","DOI":"10.1109\/SSCI.2015.33"},{"key":"e_1_3_3_2_19_2","unstructured":"K. Deepshikha and A. Naman. 2020. Removing class imbalance using Polarity-GAN: An uncertainty sampling approach. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2012.04937 (2020)."},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Q. Dong S. Gong and X. Zhu. 2017. Class rectification hard mining for imbalanced deep learning. IEEE International Conference on Computer Vision (ICCV) (2017).","DOI":"10.1109\/ICCV.2017.205"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Q. Dong S. Gong and X. Zhu. 2019. Imbalanced deep learning by minority class incremental rectification. IEEE Transactions on Pattern Analysis and Machine Intelligence 41 6 (2019) 1367\u20131381.","DOI":"10.1109\/TPAMI.2018.2832629"},{"key":"e_1_3_3_2_22_2","unstructured":"C. Drummond and R. Holte. 2003. C4.5 class imbalance and cost sensitivity: Why under-sampling beats over-sampling. ICML Workshop on Learning from Imbalanced Datasets II 11 (2003)."},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"A. Fern\u00e1ndez S. Garcia M. del Jesus and F. Herrera. 2008. A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced datasets. Fuzzy Sets and Systems 159 18 (2008) 2378\u20132398.","DOI":"10.1016\/j.fss.2007.12.023"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"A. Fern\u00e1ndez V. L\u00f3pez M. Galar M. del Jesus and F. Herrera. 2013. Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowledge-Based Systems 42 (2013) 97\u2013110.","DOI":"10.1016\/j.knosys.2013.01.018"},{"key":"e_1_3_3_2_25_2","unstructured":"J. Fisher and K. McEvoy. 2022. Bayesian multinomial logistic regression for numerous categories. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2208.14537 (2022)."},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"M. Galar A. Fern\u00e1ndez E. Barrenechea and F. Herrera. 2013. EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recognition 46 12 (2013) 3460\u20133471.","DOI":"10.1016\/j.patcog.2013.05.006"},{"key":"e_1_3_3_2_27_2","unstructured":"V. Ganganwar. 2012. An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering 2 4 (2012) 42\u201347."},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"S. Garcia and F. Herrera. 2009. Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy. Evolutionary Computation 17 3 (2009) 275\u2013306.","DOI":"10.1162\/evco.2009.17.3.275"},{"key":"e_1_3_3_2_29_2","unstructured":"J. Gonzalez Z. Dai A. Damianou and N. Lawrence. 2017. Preferential Bayesian optimization. International Conference on Machine Learning (ICML) (2017)."},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"H. Guo and H. Viktor. 2004. Learning from imbalanced data sets with boosting and data generation: The DataBoost-IM approach. ACM Sigkdd Explorations Newsletter 6 1 (2004).","DOI":"10.1145\/1007730.1007736"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"crossref","unstructured":"G. Haixiang L. Yijing J. Shang G. Mingyun and H. Yuanyue. 2017. Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications 73 (2017) 220\u2013239.","DOI":"10.1016\/j.eswa.2016.12.035"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"crossref","unstructured":"H. He and E. Garcia. 2009. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21 9 (2009) 1263\u20131284.","DOI":"10.1109\/TKDE.2008.239"},{"key":"e_1_3_3_2_33_2","unstructured":"J. Hidalgo G. Bringas E. Sanz and F. Garcia. 2006. Content based SMS spam filtering. ACM symposium on Document engineering (2006) 107\u2013114."},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"crossref","unstructured":"C. Huang Y. Li C. Loy and X. Tang. 2016. Learning deep representation for imbalanced classification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016).","DOI":"10.1109\/CVPR.2016.580"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"crossref","unstructured":"C. Huang Y. Li and C.\u00a0Loy\u00a0X. Tang. 2019. Deep imbalanced learning for face recognition and attribute prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence 42 11 (2019) 2781\u20132794.","DOI":"10.1109\/TPAMI.2019.2914680"},{"key":"e_1_3_3_2_36_2","unstructured":"J. Huggins T. Campbell and T. Broderick. 2016. Coresets for scalable Bayesian logistic regression. Advances in Neural Information Processing Systems (NeurIPS) (2016)."},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"crossref","unstructured":"J.\u00a0Van Hulse T. Khoshgoftaar and A. Napolitano. 2007. Experimental perspectives on learning from imbalanced data. International Conference on Machine Learning (ICML) (2007) 935\u2013942.","DOI":"10.1145\/1273496.1273614"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"crossref","unstructured":"N. Japkowicz and S. Stephen. 2002. The class imbalance problem: A systematic study. Intelligent data analysis 6 5 (2002) 429\u2013449.","DOI":"10.3233\/IDA-2002-6504"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"crossref","unstructured":"P. Jeatrakul K. Wong and C. Fung. 2010. Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm. International Conference on Neural Information Processing (ICONIP). Models and Applications 17 (2010) 152\u2013159.","DOI":"10.1007\/978-3-642-17534-3_19"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"crossref","unstructured":"S. Jiang J. Li Y. Wang B. Huang Z. Zhang and T. Xu. 2022. Delving into sample loss curve to embrace noisy and imbalanced data. AAAI Conference on Artificial Intelligence 36 6 (2022) 7024\u20137032.","DOI":"10.1609\/aaai.v36i6.20661"},{"key":"e_1_3_3_2_41_2","unstructured":"B. Kang S. Xie M. Rohrbach Z. Yan A. Gordo J. Feng and Y. Kalantidis. 2020. Decoupling representation and classifier for long-tailed recognition. International Conference on Learning Representations (ICLR) (2020)."},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"crossref","unstructured":"S. Khan M. Hayat M. Bennamoun F. Sohel and R. Togneri. 2017. Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Transactions on Neural Networks and Learning Systems 29 8 (2017) 3573\u20133587.","DOI":"10.1109\/TNNLS.2017.2732482"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"crossref","unstructured":"J. Kim J. Jeong and J. Shin. 2020. M2m: Imbalanced classification via major-to-minor translation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020).","DOI":"10.1109\/CVPR42600.2020.01391"},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"crossref","unstructured":"B. Krawczyk. 2016. Learning from imbalanced data: Open challenges and future directions. Progress in Artificial Intelligence 5 4 (2016) 221\u2013232.","DOI":"10.1007\/s13748-016-0094-0"},{"key":"e_1_3_3_2_45_2","doi-asserted-by":"crossref","unstructured":"B. Krawczyk M. Wozniak and G. Schaefer. 2014. Cost-sensitive decision tree ensembles for effective imbalanced classification. Applied Soft Computing 14 (2014) 554\u2013562.","DOI":"10.1016\/j.asoc.2013.08.014"},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"crossref","unstructured":"H. Kushner. 1964. A new method for locating the maximum point of an arbitrary multipeak curve in the presence of noise. Journal of Basic Engineering 86 (1964).","DOI":"10.1115\/1.3653121"},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"crossref","unstructured":"H. Lee M. Park and J. Kim. 2016. Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning. IEEE international conference on image processing (ICIP) (2016) 3713\u20133717.","DOI":"10.1109\/ICIP.2016.7533053"},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"crossref","unstructured":"J. Leevy T. Khoshgoftaar R. Bauder and N. Seliya. 2018. A survey on addressing high-class imbalance in big data. Journal of Big Data 5 (2018).","DOI":"10.1186\/s40537-018-0151-6"},{"key":"e_1_3_3_2_49_2","unstructured":"G. Lemaitre2017a F. Nogueira and C. Aridas. 2017. Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. Journal of Machine Learning Research (JMLR) 18 17 (2017) 1\u20135."},{"key":"e_1_3_3_2_50_2","doi-asserted-by":"crossref","unstructured":"T. Lin P. Goyal R. Girshick K. He and P. Dollar. 2017. Focal loss for dense object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017).","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_3_2_51_2","doi-asserted-by":"crossref","unstructured":"W. Lin C. Tsai Y. Hu and J. Jhang. 2017. Clustering-based undersampling in class-imbalanced data. Information Sciences 409 (2017) 17\u201326.","DOI":"10.1016\/j.ins.2017.05.008"},{"key":"e_1_3_3_2_52_2","doi-asserted-by":"crossref","unstructured":"X. Liu J. Wu and Z. Zhou. 2009. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 39 2 (2009) 539\u2013550.","DOI":"10.1109\/TSMCB.2008.2007853"},{"key":"e_1_3_3_2_53_2","unstructured":"D. Lizotte. 2008. Practical Bayesian optimization. PhD thesis University of Alberta (2008)."},{"key":"e_1_3_3_2_54_2","unstructured":"D. Lizotte T. Wang M. Bowling and D. Schuurmans. 2007. Automatic gait optimization with Gaussian process regression. International Joint Conference on Artificial Intelligence (IJCAI) (2007)."},{"key":"e_1_3_3_2_55_2","doi-asserted-by":"crossref","unstructured":"M. Maalouf and T. Trafalis. 2011. Robust weighted kernel logistic regression in imbalanced and rare events data. Computational Statistics & Data Analysis 55 1 (2011) 168\u2013183.","DOI":"10.1016\/j.csda.2010.06.014"},{"key":"e_1_3_3_2_56_2","doi-asserted-by":"crossref","unstructured":"T. Maciejewski and J. Stefanowski. 2011. Local neighbourhood extension of SMOTE for mining imbalanced data. IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (2011).","DOI":"10.1109\/CIDM.2011.5949434"},{"key":"e_1_3_3_2_57_2","unstructured":"G. Malkomes and R. Garnett. 2018. Automating Bayesian optimization with Bayesian optimization. Advances in Neural Information Processing Systems (NeurIPS) (2018)."},{"key":"e_1_3_3_2_58_2","unstructured":"I. Mani and J. Zhang. 2003. kNN approach to unbalanced data distributions: A case study involving information extraction. Proceedings of Workshop on Learning from Imbalanced Datasets 126 (2003) 1\u20137."},{"key":"e_1_3_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-009-0909-0"},{"key":"e_1_3_3_2_60_2","doi-asserted-by":"crossref","unstructured":"S. Mullick S. Datta and S. Das. 2019. Generative adversarial minority oversampling. IEEE International Conference on Computer Vision (ICCV) (2019).","DOI":"10.1109\/ICCV.2019.00178"},{"key":"e_1_3_3_2_61_2","doi-asserted-by":"crossref","unstructured":"H. Nguyen E. Cooper and K. Kamei. 2011. Borderline over-sampling for imbalanced data classification. International Journal of Knowledge Engineering and Soft Data Paradigms 3 1 (2011) 4\u201321.","DOI":"10.1504\/IJKESDP.2011.039875"},{"key":"e_1_3_3_2_62_2","doi-asserted-by":"crossref","unstructured":"S. O\u2019Brien and D. Dunson. 2004. Bayesian multivariate logistic regression. Biometrics 60 3 (2004) 739\u2013746.","DOI":"10.1111\/j.0006-341X.2004.00224.x"},{"key":"e_1_3_3_2_63_2","unstructured":"M. Ochal M. Patacchiola J. Vazquez A. Storkey and S. Wang. 2021. Few-shot learning with class imbalance. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2101.02523 (2021)."},{"key":"e_1_3_3_2_64_2","unstructured":"S. Parambath N. Usunier and Y. Grandvalet. 2014. Optimizing f-measures by cost-sensitive classification. Advances in Neural Information Processing Systems (NeurIPS) (2014)."},{"key":"e_1_3_3_2_65_2","doi-asserted-by":"crossref","unstructured":"M. Peng Q. Zhang X. Xing T. Gui X. Huang Y. Jiang K. Ding and Z. Chen. 2019. Trainable undersampling for class-imbalance learning. AAAI Conference on Artificial Intelligence 33 1 (2019) 4707\u20134714.","DOI":"10.1609\/aaai.v33i01.33014707"},{"key":"e_1_3_3_2_66_2","doi-asserted-by":"crossref","unstructured":"S. Pouyanfar Y. Tao A. Mohan H. Tian A. Kaseb K. Gauen R. Dailey S. Aghajanzadeh Y. Lu S. Chen and M. Shyu. 2018. Dynamic sampling in convolutional neural networks for imbalanced data classification. IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (2018) 112\u2013117.","DOI":"10.1109\/MIPR.2018.00027"},{"key":"e_1_3_3_2_67_2","volume-title":"Gaussian processes for machine learning","author":"Rasmussen C.","year":"2006","unstructured":"C. Rasmussen and C. Williams. 2006. Gaussian processes for machine learning. MIT Press."},{"key":"e_1_3_3_2_68_2","doi-asserted-by":"crossref","unstructured":"P. Shamsolmoali M. Zareapoor L. Shen A. Sadka and J. Yang. 2020. Imbalanced data learning by minority class augmentation using capsule adversarial networks. Neurocomputing 459 (2020) 481\u2013493.","DOI":"10.1016\/j.neucom.2020.01.119"},{"key":"e_1_3_3_2_69_2","unstructured":"J. Snoek H. Larochelle and R. Adams. 2012. Practical Bayesian optimization of machine learning algorithms. Advances in Neural Information Processing Systems (NeurIPS) (2012)."},{"key":"e_1_3_3_2_70_2","doi-asserted-by":"crossref","unstructured":"J. Stefanowski. 2016. Dealing with data difficulty factors while learning from imbalanced data. Challenges in Computational Statistics and Data Mining (2016) 333\u2013363.","DOI":"10.1007\/978-3-319-18781-5_17"},{"key":"e_1_3_3_2_71_2","doi-asserted-by":"crossref","unstructured":"Y. Sun A. Wong and M. Kamel. 2009. Classification of imbalanced data: A review. International Journal of Pattern Recognition and Artificial Intelligence 23 4 (2009) 687\u2013719.","DOI":"10.1142\/S0218001409007326"},{"key":"e_1_3_3_2_72_2","doi-asserted-by":"crossref","unstructured":"E. Swana W. Doorsamy and P. Bokoro. 2022. Tomek link and SMOTE approaches for machine fault classification with an imbalanced dataset. Sensors 22 9 (2022).","DOI":"10.3390\/s22093246"},{"key":"e_1_3_3_2_73_2","unstructured":"K. Swersky J. Snoek and R. Adams. 2013. Multi-task Bayesian optimization. Advances in Neural Information Processing Systems (NeurIPS) (2013)."},{"key":"e_1_3_3_2_74_2","doi-asserted-by":"crossref","unstructured":"Y. Tang Y. Zhang N. Chawla and S. Krasser. 2008. SVMs modeling for highly imbalanced classification. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 39 1 (2008) 281\u2013288.","DOI":"10.1109\/TSMCB.2008.2002909"},{"key":"e_1_3_3_2_75_2","doi-asserted-by":"crossref","unstructured":"K. Ting. 2000. A comparative study of cost-sensitive boosting algorithms. International Conference on Machine Learning (ICML) (2000).","DOI":"10.1007\/3-540-45164-1_42"},{"key":"e_1_3_3_2_76_2","doi-asserted-by":"crossref","unstructured":"I. Tomek. 1976. An experiment with the edited nearest neighbor rule. IEEE Transactions on Systems Man and Cybernetics 6 (1976) 448\u2013452.","DOI":"10.1109\/TSMC.1976.4309523"},{"key":"e_1_3_3_2_77_2","doi-asserted-by":"crossref","unstructured":"I. Tomek. 1976. Two modifications of CNN. IEEE Transactions on Systems Man and Cybernetics 6 (1976) 769\u2013772.","DOI":"10.1109\/TSMC.1976.4309452"},{"key":"e_1_3_3_2_78_2","doi-asserted-by":"crossref","unstructured":"C. Tsai W. Lin Y. Hu and G. Yao. 2019. Under-sampling class imbalanced datasets by combining clustering analysis and instance selection. Information Sciences 477 (2019) 47\u201354.","DOI":"10.1016\/j.ins.2018.10.029"},{"key":"e_1_3_3_2_79_2","unstructured":"Y. Wang D. Ramanan and M. Hebert. 2017. Learning to model the tail. Advances in Neural Information Processing Systems (NeurIPS) (2017)."},{"key":"e_1_3_3_2_80_2","doi-asserted-by":"crossref","unstructured":"C. Williams. 1999. Prediction with Gaussian processes: From linear regression to linear prediction and beyond. Learning in Graphical Models (1999).","DOI":"10.1007\/978-94-011-5014-9_23"},{"key":"e_1_3_3_2_81_2","doi-asserted-by":"crossref","unstructured":"X. Yang Q. Kuang W. Zhang and G. Zhang. 2017. AMDO: An over-sampling technique for multi-class imbalanced problems. IEEE Transactions on Knowledge and Data Engineering 30 9 (2017) 1672\u20131685.","DOI":"10.1109\/TKDE.2017.2761347"},{"key":"e_1_3_3_2_82_2","unstructured":"Y. Yang and Z. Xu. 2020. Rethinking the value of labels for improving class-imbalanced learning. Advances in Neural Information Processing Systems (NeurIPS) (2020)."},{"key":"e_1_3_3_2_83_2","unstructured":"X. Yin X. Yu K. Sohn X. Liu and M. Chandraker. 2018. Feature transfer learning for deep face recognition with long-tail data. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1803.09014 (2018)."},{"key":"e_1_3_3_2_84_2","doi-asserted-by":"crossref","unstructured":"B. Zadrozny J. Langford and N. Abe. 2003. Cost-sensitive learning by cost-proportionate example weighting. IEEE international conference on data mining (ICDM) (2003) 435\u2013442.","DOI":"10.1109\/ICDM.2003.1250950"},{"key":"e_1_3_3_2_85_2","doi-asserted-by":"crossref","unstructured":"M. Zheng T. Li L. Sun T. Wang B. Jie W. Yang M. Tang and C. Lv. 2021. An automatic sampling ratio detection method based on genetic algorithm for imbalanced data classification. Knowledge-Based Systems 216 (2021).","DOI":"10.1016\/j.knosys.2021.106800"}],"event":{"name":"ICAAI 2024: 2024 The 8th International Conference on Advances in Artificial Intelligence","acronym":"ICAAI 2024","location":"London United Kingdom"},"container-title":["Proceedings of the 2024 8th International Conference on Advances in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3704137.3704193","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3704137.3704193","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:07Z","timestamp":1750295887000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3704137.3704193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,17]]},"references-count":84,"alternative-id":["10.1145\/3704137.3704193","10.1145\/3704137"],"URL":"https:\/\/doi.org\/10.1145\/3704137.3704193","relation":{},"subject":[],"published":{"date-parts":[[2024,10,17]]},"assertion":[{"value":"2025-03-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}