This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., …, & Hussain, A. (2016). Comparing oversampling techniques to handle the class imbalance problem: A customer churn prediction case study. IEEE Access, 4(Ml), 7940–7957. https://doi.org/10.1109/ACCESS.2016.2619719AminA.AnwarS.AdnanA.NawazM.HowardN.QadirJ.HussainA.2016Comparing oversampling techniques to handle the class imbalance problem: A customer churn prediction case studyIEEE Access4Ml79407957https://doi.org/10.1109/ACCESS.2016.261971910.1109/ACCESS.2016.2619719Search in Google Scholar
El-Sayed, A.A., Mahmood, M.A.M., Meguid, N.A., & Hefny, H.A. (2016). Handling autism imbalanced data using synthetic minority over-sampling technique (SMOTE). In Proceedings of 2015 IEEE World Conference on Complex Systems, WCCS 2015, (November). https://doi.org/10.1109/ICoCS.2015.7483267El-SayedA.A.MahmoodM.A.M.MeguidN.A.HefnyH.A.2016Handling autism imbalanced data using synthetic minority over-sampling technique (SMOTE)InProceedings of 2015 IEEE World Conference on Complex Systems, WCCS2015, (November)https://doi.org/10.1109/ICoCS.2015.748326710.1109/ICoCS.2015.7483267Search in Google Scholar
Fotouhi, S., Asadi, S., & Kattan, M.W. (2019). A comprehensive data level analysis for cancer diagnosis on imbalanced data. Journal of Biomedical Informatics, 90. https://doi.org/10.1016/j.jbi.2018.12.003FotouhiS.AsadiS.KattanM.W.2019A comprehensive data level analysis for cancer diagnosis on imbalanced dataJournal of Biomedical Informatics90https://doi.org/10.1016/j.jbi.2018.12.00310.1016/j.jbi.2018.12.003Search in Google Scholar
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 42(4), 463–484. https://doi.org/10.1109/TSMCC.2011.2161285GalarM.FernandezA.BarrenecheaE.BustinceH.HerreraF.2012A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approachesIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews424463484https://doi.org/10.1109/TSMCC.2011.216128510.1109/TSMCC.2011.2161285Search in Google Scholar
He, H., Bai, Y., Garcia, E., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence), Hong Kong, 2008, pp. 1322–1328. doi: 10.1109/IJCNN.2008.4633969HeH.BaiY.GarciaE.LiS.2008ADASYN: Adaptive synthetic sampling approach for imbalanced learninInIEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence)Hong Kong20081322132810.1109/IJCNN.2008.4633969Open DOISearch in Google Scholar
Kheirkhah, P., Feng, Q., Travis, L.M., Tavakoli-Tabasi, S., & Sharafkhaneh, A. (2016). Prevalence, predictors and economic consequences of no-shows. BMC Health Services Research, 16(1), 1–6. https://doi.org/10.1186/s12913-015-1243-zKheirkhahP.FengQ.TravisL.M.Tavakoli-TabasiS.SharafkhanehA.2016Prevalence, predictors and economic consequences of no-showsBMC Health Services Research16116https://doi.org/10.1186/s12913-015-1243-z10.1186/s12913-015-1243-zSearch in Google Scholar
Lemnaru, C., & Potolea, R. (2012). Imbalanced classification problems: Systematic study, issues and best practices. Lecture Notes in Business Information Processing, 102 LNBIP(1), 35–50. https://doi.org/10.1007/978-3-642-29958-2_3LemnaruC.PotoleaR.2012Imbalanced classification problems: Systematic study, issues and best practicesLecture Notes in Business Information Processing102LNBIP13550https://doi.org/10.1007/978-3-642-29958-2_310.1007/978-3-642-29958-2_3Search in Google Scholar
López, V., Fernández, A., García, S., Palade, V., & Herrera, F. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information Sciences, 250, 113–141. https://doi.org/10.1016/j.ins.2013.07.007LópezV.FernándezA.GarcíaS.PaladeV.HerreraF.2013An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristicsInformation Sciences250113141https://doi.org/10.1016/j.ins.2013.07.00710.1016/j.ins.2013.07.007Search in Google Scholar
Mehndiratta, P., & Soni, D. (2019). Identification of sarcasm in textual data: A comparative study. Journal of Data and Information Science, 4(4), 56–83. https://doi.org/10.2478/jdis-2019-0021MehndirattaP.SoniD.2019Identification of sarcasm in textual data: A comparative studyJournal of Data and Information Science445683https://doi.org/10.2478/jdis-2019-002110.2478/jdis-2019-0021Search in Google Scholar
Mohammadi, I., Wu, H., Turkcan, A., Toscos, T., & Doebbeling, B.N. (2018). Data analytics and modeling for appointment no-show in community health centers. Journal of Primary Care and Community Health, 9. https://doi.org/10.1177/2150132718811692MohammadiI.WuH.TurkcanA.ToscosT.DoebbelingB.N.2018Data analytics and modeling for appointment no-show in community health centersJournal of Primary Care and Community Health9https://doi.org/10.1177/215013271881169210.1177/2150132718811692Search in Google Scholar
Zhao, Y., Wong, Z.S.Y., & Tsui, K.L. (2018). A framework of rebalancing imbalanced healthcare data for rare events’ classification: A case of look-alike sound-alike mix-up incident detection. Journal of Healthcare Engineering, 2018(2010), 1–11. https://doi.org/10.1155/2018/6275435ZhaoY.WongZ.S.Y.TsuiK.L.2018A framework of rebalancing imbalanced healthcare data for rare events’ classification: A case of look-alike sound-alike mix-up incident detectionJournal of Healthcare Engineering20182010111https://doi.org/10.1155/2018/627543510.1155/2018/6275435Search in Google Scholar
