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For: Santos MS, Abreu PH, García-Laencina PJ, Simão A, Carvalho A. A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients. J Biomed Inform 2015;58:49-59. [PMID: 26423562 DOI: 10.1016/j.jbi.2015.09.012] [Cited by in Crossref: 67] [Cited by in F6Publishing: 21] [Article Influence: 9.6] [Reference Citation Analysis]
Number Citing Articles
1 Santos MS, Abreu PH, Fernández A, Luengo J, Santos J. The impact of heterogeneous distance functions on missing data imputation and classification performance. Engineering Applications of Artificial Intelligence 2022;111:104791. [DOI: 10.1016/j.engappai.2022.104791] [Reference Citation Analysis]
2 Bania RK, Halder A. R-HEFS: Rough set based heterogeneous ensemble feature selection method for medical data classification. Artif Intell Med 2021;114:102049. [PMID: 33875164 DOI: 10.1016/j.artmed.2021.102049] [Reference Citation Analysis]
3 Tao X, Zheng Y, Chen W, Zhang X, Qi L, Fan Z, Huang S. SVDD-based weighted oversampling technique for imbalanced and overlapped dataset learning. Information Sciences 2022;588:13-51. [DOI: 10.1016/j.ins.2021.12.066] [Reference Citation Analysis]
4 Lynch CM, van Berkel VH, Frieboes HB. Application of unsupervised analysis techniques to lung cancer patient data. PLoS One 2017;12:e0184370. [PMID: 28910336 DOI: 10.1371/journal.pone.0184370] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 3.6] [Reference Citation Analysis]
5 Feng XF, Yang LC, Tan LZ, Li YG. Risk factor analysis of device-related infections: value of re-sampling method on the real-world imbalanced dataset. BMC Med Inform Decis Mak 2019;19:185. [PMID: 31511006 DOI: 10.1186/s12911-019-0899-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
6 Verij kazemi M, Fazeli Veysari E. A new optimization algorithm inspired by the quest for the evolution of human society: Human felicity algorithm. Expert Systems with Applications 2022;193:116468. [DOI: 10.1016/j.eswa.2021.116468] [Reference Citation Analysis]
7 Ali L, Wajahat I, Amiri Golilarz N, Keshtkar F, Bukhari SAC. LDA–GA–SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine. Neural Comput & Applic 2021;33:2783-92. [DOI: 10.1007/s00521-020-05157-2] [Cited by in Crossref: 34] [Cited by in F6Publishing: 14] [Article Influence: 17.0] [Reference Citation Analysis]
8 Kaur I, Doja M, Ahmad T. Data Mining and Machine Learning in Cancer Survival Research: An Overview and Future Recommendations. Journal of Biomedical Informatics 2022. [DOI: 10.1016/j.jbi.2022.104026] [Reference Citation Analysis]
9 Petropoulos F, Apiletti D, Assimakopoulos V, Babai MZ, Barrow DK, Ben Taieb S, Bergmeir C, Bessa RJ, Bijak J, Boylan JE, Browell J, Carnevale C, Castle JL, Cirillo P, Clements MP, Cordeiro C, Cyrino Oliveira FL, De Baets S, Dokumentov A, Ellison J, Fiszeder P, Franses PH, Frazier DT, Gilliland M, Gönül MS, Goodwin P, Grossi L, Grushka-cockayne Y, Guidolin M, Guidolin M, Gunter U, Guo X, Guseo R, Harvey N, Hendry DF, Hollyman R, Januschowski T, Jeon J, Jose VRR, Kang Y, Koehler AB, Kolassa S, Kourentzes N, Leva S, Li F, Litsiou K, Makridakis S, Martin GM, Martinez AB, Meeran S, Modis T, Nikolopoulos K, Önkal D, Paccagnini A, Panagiotelis A, Panapakidis I, Pavía JM, Pedio M, Pedregal DJ, Pinson P, Ramos P, Rapach DE, Reade JJ, Rostami-tabar B, Rubaszek M, Sermpinis G, Shang HL, Spiliotis E, Syntetos AA, Talagala PD, Talagala TS, Tashman L, Thomakos D, Thorarinsdottir T, Todini E, Trapero Arenas JR, Wang X, Winkler RL, Yusupova A, Ziel F. Forecasting: theory and practice. International Journal of Forecasting 2022. [DOI: 10.1016/j.ijforecast.2021.11.001] [Reference Citation Analysis]
10 Santos MS, Abreu PH, Japkowicz N, Fernández A, Soares C, Wilk S, Santos J. On the joint-effect of class imbalance and overlap: a critical review. Artif Intell Rev. [DOI: 10.1007/s10462-022-10150-3] [Reference Citation Analysis]
11 Liang X, Jiang A, Li T, Xue Y, Wang G. LR-SMOTE — An improved unbalanced data set oversampling based on K-means and SVM. Knowledge-Based Systems 2020;196:105845. [DOI: 10.1016/j.knosys.2020.105845] [Cited by in Crossref: 25] [Cited by in F6Publishing: 2] [Article Influence: 12.5] [Reference Citation Analysis]
12 Hadi W, El-Khalili N, AlNashashibi M, Issa G, AlBanna AA. Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan. Comput Biol Med 2019;114:103474. [PMID: 31585402 DOI: 10.1016/j.compbiomed.2019.103474] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 1.7] [Reference Citation Analysis]
13 Shobha K, Savarimuthu N. Clustering based imputation algorithm using unsupervised neural network for enhancing the quality of healthcare data. J Ambient Intell Human Comput 2021;12:1771-81. [DOI: 10.1007/s12652-020-02250-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
14 Chicco D, Oneto L. Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma. Health Informatics J 2021;27:1460458220984205. [PMID: 33504243 DOI: 10.1177/1460458220984205] [Reference Citation Analysis]
15 Książek W, Hammad M, Pławiak P, Acharya UR, Tadeusiewicz R. Development of novel ensemble model using stacking learning and evolutionary computation techniques for automated hepatocellular carcinoma detection. Biocybernetics and Biomedical Engineering 2020;40:1512-24. [DOI: 10.1016/j.bbe.2020.08.007] [Cited by in Crossref: 8] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
16 Parisi L, Ravichandran N. Evolutionary feature transformation to improve prognostic prediction of hepatitis. Knowledge-Based Systems 2020;200:106012. [DOI: 10.1016/j.knosys.2020.106012] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
17 Elgin Christo VR, Khanna Nehemiah H, Keerthana Sankari S, Jeyaraj S, Kannan A. Classification Framework for Clinical Datasets Using Synergistic Firefly Optimization. IETE Journal of Research. [DOI: 10.1080/03772063.2021.2007798] [Reference Citation Analysis]
18 Tao X, Chen W, Zhang X, Guo W, Qi L, Fan Z. SVDD boundary and DPC clustering technique-based oversampling approach for handling imbalanced and overlapped data. Knowledge-Based Systems 2021;234:107588. [DOI: 10.1016/j.knosys.2021.107588] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
19 Sawhney R, Mathur P, Shankar R. A Firefly Algorithm Based Wrapper-Penalty Feature Selection Method for Cancer Diagnosis. In: Gervasi O, Murgante B, Misra S, Stankova E, Torre CM, Rocha AMA, Taniar D, Apduhan BO, Tarantino E, Ryu Y, editors. Computational Science and Its Applications – ICCSA 2018. Cham: Springer International Publishing; 2018. pp. 438-49. [DOI: 10.1007/978-3-319-95162-1_30] [Cited by in Crossref: 26] [Cited by in F6Publishing: 3] [Article Influence: 6.5] [Reference Citation Analysis]
20 Kaur I, Doja MN, Ahmad T. Time-range based sequential mining for survival prediction in prostate cancer. J Biomed Inform 2020;110:103550. [PMID: 32882394 DOI: 10.1016/j.jbi.2020.103550] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
21 Książek W, Abdar M, Acharya UR, Pławiak P. A novel machine learning approach for early detection of hepatocellular carcinoma patients. Cognitive Systems Research 2019;54:116-27. [DOI: 10.1016/j.cogsys.2018.12.001] [Cited by in Crossref: 53] [Cited by in F6Publishing: 17] [Article Influence: 17.7] [Reference Citation Analysis]
22 Ma JH, Feng Z, Wu JY, Zhang Y, Di W. Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks. BMC Med Inform Decis Mak 2021;21:127. [PMID: 33845834 DOI: 10.1186/s12911-021-01486-x] [Reference Citation Analysis]
23 Chi N, Wang J, Liao J, Cheng W, Chen C. Machine learning-based seismic capability evaluation for school buildings. Automation in Construction 2020;118:103274. [DOI: 10.1016/j.autcon.2020.103274] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
24 Fernandes K, Chicco D, Cardoso JS, Fernandes J. Supervised deep learning embeddings for the prediction of cervical cancer diagnosis. PeerJ Comput Sci 2018;4:e154. [PMID: 33816808 DOI: 10.7717/peerj-cs.154] [Cited by in Crossref: 23] [Cited by in F6Publishing: 5] [Article Influence: 5.8] [Reference Citation Analysis]
25 Abreu PH, Santos MS, Abreu MH, Andrade B, Silva DC. Predicting Breast Cancer Recurrence Using Machine Learning Techniques: A Systematic Review. ACM Comput Surv 2016;49:1-40. [DOI: 10.1145/2988544] [Cited by in Crossref: 63] [Cited by in F6Publishing: 17] [Article Influence: 10.5] [Reference Citation Analysis]
26 Li K, Ren B, Guan T, Wang J, Yu J, Wang K, Huang J. A hybrid cluster-borderline SMOTE method for imbalanced data of rock groutability classification. Bull Eng Geol Environ 2022;81. [DOI: 10.1007/s10064-021-02523-9] [Reference Citation Analysis]
27 Ijaz MF, Attique M, Son Y. Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods. Sensors (Basel) 2020;20:E2809. [PMID: 32429090 DOI: 10.3390/s20102809] [Cited by in Crossref: 41] [Cited by in F6Publishing: 13] [Article Influence: 20.5] [Reference Citation Analysis]
28 Tao X, Li Q, Guo W, Ren C, He Q, Liu R, Zou J. Adaptive weighted over-sampling for imbalanced datasets based on density peaks clustering with heuristic filtering. Information Sciences 2020;519:43-73. [DOI: 10.1016/j.ins.2020.01.032] [Cited by in Crossref: 15] [Cited by in F6Publishing: 1] [Article Influence: 7.5] [Reference Citation Analysis]
29 Zou ZM, Chang DH, Liu H, Xiao YD. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know? Insights Imaging 2021;12:31. [PMID: 33675433 DOI: 10.1186/s13244-021-00977-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
30 Murugesan S, Bhuvaneswaran RS, Khanna Nehemiah H, Keerthana Sankari S, Nancy Jane Y. Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner. Comput Math Methods Med 2021;2021:6662420. [PMID: 34055041 DOI: 10.1155/2021/6662420] [Reference Citation Analysis]
31 Parisi L, Ravichandran N. Syncretic Feature Selection for Machine Learning-Aided Prognostics of Hepatitis. Neural Process Lett. [DOI: 10.1007/s11063-021-10668-7] [Reference Citation Analysis]
32 Tan X, Su S, Huang Z, Guo X, Zuo Z, Sun X, Li L. Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm. Sensors (Basel) 2019;19:E203. [PMID: 30626020 DOI: 10.3390/s19010203] [Cited by in Crossref: 30] [Cited by in F6Publishing: 7] [Article Influence: 10.0] [Reference Citation Analysis]
33 Douzas G, Bacao F, Last F. Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Information Sciences 2018;465:1-20. [DOI: 10.1016/j.ins.2018.06.056] [Cited by in Crossref: 147] [Cited by in F6Publishing: 17] [Article Influence: 36.8] [Reference Citation Analysis]
34 Chen B, Xia S, Chen Z, Wang B, Wang G. RSMOTE: A self-adaptive robust SMOTE for imbalanced problems with label noise. Information Sciences 2021;553:397-428. [DOI: 10.1016/j.ins.2020.10.013] [Cited by in Crossref: 22] [Cited by in F6Publishing: 7] [Article Influence: 22.0] [Reference Citation Analysis]
35 Christo VRE, Nehemiah HK, Brighty J, Kannan A. Feature Selection and Instance Selection from Clinical Datasets Using Co-operative Co-evolution and Classification Using Random Forest. IETE Journal of Research. [DOI: 10.1080/03772063.2020.1713917] [Cited by in Crossref: 9] [Article Influence: 4.5] [Reference Citation Analysis]
36 Liu T, Zhu X, Pedrycz W, Li Z. A design of information granule-based under-sampling method in imbalanced data classification. Soft Comput 2020;24:17333-47. [DOI: 10.1007/s00500-020-05023-2] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
37 Elyan E, Moreno-garcia CF, Jayne C. CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification. Neural Comput & Applic 2021;33:2839-51. [DOI: 10.1007/s00521-020-05130-z] [Cited by in Crossref: 15] [Cited by in F6Publishing: 3] [Article Influence: 7.5] [Reference Citation Analysis]
38 Hattab M, Maalel A, Ghezala HHB. Towards an Oversampling Method to Improve Hepatocellular Carcinoma Early Prediction. In: Chaari L, editor. Digital Health in Focus of Predictive, Preventive and Personalised Medicine. Cham: Springer International Publishing; 2020. pp. 139-48. [DOI: 10.1007/978-3-030-49815-3_16] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
39 Demir FB, Tuncer T, Kocamaz AF, Ertam F. A survival classification method for hepatocellular carcinoma patients with chaotic Darcy optimization method based feature selection. Med Hypotheses 2020;139:109626. [PMID: 32087492 DOI: 10.1016/j.mehy.2020.109626] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
40 Tuncer T, Ertam F. Neighborhood component analysis and reliefF based survival recognition methods for Hepatocellular carcinoma. Physica A: Statistical Mechanics and its Applications 2020;540:123143. [DOI: 10.1016/j.physa.2019.123143] [Cited by in Crossref: 14] [Article Influence: 7.0] [Reference Citation Analysis]
41 Turabieh H, Abu Salem A, Abu-el-rub N. Dynamic L-RNN recovery of missing data in IoMT applications. Future Generation Computer Systems 2018;89:575-83. [DOI: 10.1016/j.future.2018.07.006] [Cited by in Crossref: 18] [Cited by in F6Publishing: 4] [Article Influence: 4.5] [Reference Citation Analysis]
42 Ye X, Li H, Sakurai T, Shueng PW. Ensemble Feature Learning to Identify Risk Factors for Predicting Secondary Cancer. Int J Med Sci 2019;16:949-59. [PMID: 31341408 DOI: 10.7150/ijms.33820] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
43 Wei J, Huang H, Yao L, Hu Y, Fan Q, Huang D. New imbalanced bearing fault diagnosis method based on Sample-characteristic Oversampling TechniquE (SCOTE) and multi-class LS-SVM. Applied Soft Computing 2021;101:107043. [DOI: 10.1016/j.asoc.2020.107043] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 6.0] [Reference Citation Analysis]
44 Parisi L, Ravichandran N, Manaog ML. A novel hybrid algorithm for aiding prediction of prognosis in patients with hepatitis. Neural Comput & Applic 2020;32:3839-52. [DOI: 10.1007/s00521-019-04050-x] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
45 Novitasari DCR, Foeady AZ, Nariswari R, Asyhar AH, Ulinnuha N, Farida Y, Santi DR, Ilham, Setiawan F. Whirlwind Classification with Imbalanced Upper Air Data Handling using SMOTE Algorithm and SVM Classifier. J Phys : Conf Ser 2020;1501:012010. [DOI: 10.1088/1742-6596/1501/1/012010] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]