{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T10:22:48Z","timestamp":1782814968890,"version":"3.54.5"},"reference-count":77,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007086","name":"Sel\u00e7uk \u00dcniversitesi","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007086","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1016\/j.asoc.2025.112819","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T16:27:19Z","timestamp":1739204839000},"page":"112819","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["A synergistic oversampling technique with differential evolution and safe level synthetic minority oversampling"],"prefix":"10.1016","volume":"172","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5596-6767","authenticated-orcid":false,"given":"Ahmet Cevahir","family":"Cinar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2025.112819_bib1","doi-asserted-by":"crossref","first-page":"9145","DOI":"10.1109\/ACCESS.2016.2647238","article-title":"A hybrid feature selection with ensemble classification for imbalanced healthcare data: a case study for brain tumor diagnosis","volume":"4","author":"Huda","year":"2016","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2025.112819_bib2","article-title":"Class-imbalanced deep learning via a class-balanced ensemble","author":"Chen","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2025.112819_bib3","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.actaastro.2020.12.012","article-title":"Imbalanced satellite telemetry data anomaly detection model based on Bayesian LSTM","volume":"180","author":"Chen","year":"2021","journal-title":"Acta Astronaut."},{"issue":"3","key":"10.1016\/j.asoc.2025.112819_bib4","first-page":"679","article-title":"Network traffic anomaly detection method for imbalanced data","volume":"33","author":"Shuqin","year":"2021","journal-title":"J. Syst. Simul."},{"key":"10.1016\/j.asoc.2025.112819_bib5","first-page":"1","article-title":"Credit card fraud detection under extreme imbalanced data: a comparative study of data-level algorithms","author":"Singh","year":"2021","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"10.1016\/j.asoc.2025.112819_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114750","article-title":"A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection","volume":"175","author":"Li","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2025.112819_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106262","article-title":"Imbalanced credit risk evaluation based on multiple sampling, multiple kernel fuzzy self-organizing map and local accuracy ensemble","volume":"91","author":"Wang","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2025.112819_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114744","article-title":"A new hybrid ensemble model with voting-based outlier detection and balanced sampling for credit scoring","volume":"174","author":"Zhang","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2025.112819_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2021.05.029","article-title":"Impact of resampling methods and classification models on the imbalanced credit scoring problems","author":"Xiao","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2025.112819_bib10","series-title":"in Journal of Physics: Conference Series","article-title":"An Analysis of Multiclass Imbalanced Data Problem in Machine Learning for Network Attack Detections","author":"Soon","year":"2021"},{"issue":"4","key":"10.1016\/j.asoc.2025.112819_bib11","doi-asserted-by":"crossref","first-page":"177","DOI":"10.18201\/ijisae.2020466310","article-title":"Pneumonia detection and classification using deep learning on chest X-ray images","volume":"8","author":"Darici","year":"2020","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"10.1016\/j.asoc.2025.112819_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114756","article-title":"Customer purchase prediction from the perspective of imbalanced data: a machine learning framework based on factorization machine","volume":"173","author":"Chen","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2025.112819_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2020.106544","article-title":"Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM","volume":"125","author":"Kong","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.asoc.2025.112819_bib14","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.patcog.2017.12.017","article-title":"A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data","volume":"77","author":"Yuan","year":"2018","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2025.112819_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114595","article-title":"Machine learning based methods for software fault prediction: a survey","author":"Pandey","year":"2021","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.asoc.2025.112819_bib16","first-page":"23","article-title":"A computer-aided detection system for breast cancer detection and classification","volume":"20","author":"Fadhil","year":"2021","journal-title":"Selcuk. Univ. J. Eng. Sci."},{"key":"10.1016\/j.asoc.2025.112819_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114751","article-title":"Hybrid ensemble approaches to online harassment detection in highly imbalanced data","volume":"175","author":"Tolba","year":"2021","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.asoc.2025.112819_bib18","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.eij.2020.02.007","article-title":"A predictive machine learning application in agriculture: cassava disease detection and classification with imbalanced dataset using convolutional neural networks","volume":"22","author":"Sambasivam","year":"2021","journal-title":"Egypt. Inform. J."},{"issue":"1","key":"10.1016\/j.asoc.2025.112819_bib19","doi-asserted-by":"crossref","first-page":"57","DOI":"10.30699\/fhi.v10i1.259","article-title":"Machine learning based methods for handling imbalanced data in hepatitis diagnosis","volume":"10","author":"Orooji","year":"2021","journal-title":"Front. Health Inform."},{"key":"10.1016\/j.asoc.2025.112819_bib20","article-title":"A hybrid multi-class imbalanced learning method for predicting the quality level of diesel engines","author":"Qin","year":"2021","journal-title":"J. Manuf. Syst."},{"issue":"1","key":"10.1016\/j.asoc.2025.112819_bib21","doi-asserted-by":"crossref","first-page":"18224","DOI":"10.1038\/s41598-024-69109-9","article-title":"Customised-sampling approach for pipe failure prediction in water distribution networks","volume":"14","author":"Latifi","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.asoc.2025.112819_bib22","doi-asserted-by":"crossref","first-page":"13758","DOI":"10.1038\/s41598-022-16830-y","article-title":"Semi-supervised learning framework for oil and gas pipeline failure detection","volume":"12","author":"Alobaidi","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2025.112819_bib23","series-title":"2021 International Conference on Computer Communication and Informatics (ICCCI)","article-title":"Class Imbalanced Data: Open Issues and Future Research Directions","author":"Rekha","year":"2021"},{"key":"10.1016\/j.asoc.2025.112819_bib24","series-title":"in Pacific-Asia conference on knowledge discovery and data mining","article-title":"Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem","author":"Bunkhumpornpat","year":"2009"},{"key":"10.1016\/j.asoc.2025.112819_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.114482","article-title":"DEBOHID: A differential evolution based oversampling approach for highly imbalanced datasets","volume":"169","author":"Kaya","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2025.112819_bib26","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.asoc.2025.112819_bib27","series-title":"in International conference on intelligent computing","article-title":"Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning","author":"Han","year":"2005"},{"issue":"12","key":"10.1016\/j.asoc.2025.112819_bib28","doi-asserted-by":"crossref","first-page":"10915","DOI":"10.1007\/s13369-020-04872-1","article-title":"Training feed-forward multi-layer perceptron artificial neural networks with a tree-seed algorithm","volume":"45","author":"Cinar","year":"2020","journal-title":"Arab. J. Sci. Eng."},{"key":"10.1016\/j.asoc.2025.112819_bib29","first-page":"1","article-title":"Binary crow search algorithm for the uncapacitated facility location problem","author":"Sonu\u00e7","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.asoc.2025.112819_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106560","article-title":"A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems","volume":"96","author":"Karakoyun","year":"2020","journal-title":"Appl. Soft Comput."},{"issue":"3","key":"10.1016\/j.asoc.2025.112819_bib31","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1007\/s00500-020-05284-x","article-title":"A novel local search method for LSGO with golden ratio and dynamic search step","volume":"25","author":"Ko\u00e7er","year":"2021","journal-title":"Soft Comput."},{"issue":"9","key":"10.1016\/j.asoc.2025.112819_bib32","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1007\/s13042-018-0878-6","article-title":"Boosting galactic swarm optimization with ABC","volume":"10","author":"Kaya","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.asoc.2025.112819_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107349","article-title":"A discrete spotted hyena optimizer for solving distributed job shop scheduling problems","volume":"106","author":"\u015eahman","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2025.112819_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107787","article-title":"Boosting the oversampling methods based on differential evolution strategies for imbalanced learning","volume":"112","author":"Korkmaz","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2025.112819_bib35","series-title":"in The 2010 International joint conference on neural networks (IJCNN)","article-title":"Cost-sensitive learning methods for imbalanced data","author":"Thai-Nghe","year":"2010"},{"key":"10.1016\/j.asoc.2025.112819_bib36","series-title":"in Pacific-Asia conference on knowledge discovery and data mining","article-title":"A PSO-based cost-sensitive neural network for imbalanced data classification","author":"Cao","year":"2013"},{"key":"10.1016\/j.asoc.2025.112819_bib37","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.12680","article-title":"A novel cost-sensitive algorithm and new evaluation strategies for regression in imbalanced domains","author":"Sadouk","year":"2021","journal-title":"Expert Syst."},{"key":"10.1016\/j.asoc.2025.112819_bib38","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2021.3083490","article-title":"A novel method for credit scoring based on cost-sensitive neural network ensemble","author":"Yotsawat","year":"2021","journal-title":"IEEE Access"},{"issue":"10","key":"10.1016\/j.asoc.2025.112819_bib39","doi-asserted-by":"crossref","first-page":"12939","DOI":"10.1016\/j.eswa.2011.04.090","article-title":"A genetic algorithm-based approach to cost-sensitive bankruptcy prediction","volume":"38","author":"Chen","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2025.112819_bib40","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.104938","article-title":"A novel test-cost-sensitive attribute reduction approach using the binary bat algorithm","volume":"186","author":"Xie","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.asoc.2025.112819_bib41","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.cor.2018.03.005","article-title":"Cost-sensitive feature selection for support vector machines","volume":"106","author":"Ben\u00edtez-Pe\u00f1a","year":"2019","journal-title":"Comput. Oper. Res."},{"key":"10.1016\/j.asoc.2025.112819_bib42","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.eswa.2019.06.044","article-title":"Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm","volume":"137","author":"Zhang","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2025.112819_bib43","series-title":"Data Management, Analytics and Innovation","first-page":"323","article-title":"Designing a Model to Handle Imbalance Data Classification Using SMOTE and Optimized Classifier","author":"Nimankar","year":"2021"},{"key":"10.1016\/j.asoc.2025.112819_bib44","series-title":"2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence)","article-title":"ADASYN: Adaptive synthetic sampling approach for imbalanced learning","author":"He","year":"2008"},{"issue":"1","key":"10.1016\/j.asoc.2025.112819_bib45","first-page":"105","article-title":"Study of bagging application in the safe-level smote method in handling unbalanced classification: kajian penerapan bagging pada metode safe-level smote dalam penanganan klasifikasi kelas tidak seimbang","volume":"5","author":"Meidianingsih","year":"2021","journal-title":"Indones. J. Stat. Its Appl."},{"key":"10.1016\/j.asoc.2025.112819_bib46","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.ins.2020.10.013","article-title":"RSMOTE: a self-adaptive robust SMOTE for imbalanced problems with label noise","volume":"553","author":"Chen","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2025.112819_bib47","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.ins.2021.03.042","article-title":"The improved AdaBoost algorithms for imbalanced data classification","volume":"563","author":"Wang","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2025.112819_bib48","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.asoc.2019.02.028","article-title":"SSOMaj-SMOTE-SSOMin: Three-step intelligent pruning of majority and minority samples for learning from imbalanced datasets","volume":"78","author":"Susan","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2025.112819_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107447","article-title":"Experimental evaluation of ensemble classifiers for imbalance in Big Data","author":"Juez-Gil","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2025.112819_bib50","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.107043","article-title":"New imbalanced bearing fault diagnosis method based on sample-characteristic oversampling techniquE (SCOTE) and multi-class LS-SVM","volume":"101","author":"Wei","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2025.112819_bib51","article-title":"SMOTE-NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution","author":"Li","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.asoc.2025.112819_bib52","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.ins.2021.03.041","article-title":"A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors","volume":"565","author":"Li","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2025.112819_bib53","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108839","article-title":"An improved and random synthetic minority oversampling technique for imbalanced data","volume":"248","author":"Wei","year":"2022","journal-title":"Knowl. Based Syst."},{"issue":"7","key":"10.1016\/j.asoc.2025.112819_bib54","doi-asserted-by":"crossref","first-page":"3424","DOI":"10.3390\/app12073424","article-title":"An oversampling method for class imbalance problems on large datasets","volume":"12","author":"Rodr\u00edguez-Torres","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.asoc.2025.112819_bib55","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110952","article-title":"An oversampling method based on differential evolution and natural neighbors","volume":"149","author":"Wang","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2025.112819_bib56","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121039","article-title":"A new oversampling approach based differential evolution on the safe set for highly imbalanced datasets","volume":"234","author":"Zhang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2025.112819_bib57","first-page":"1","article-title":"Class imbalance problem: a wrapper-based approach using under-sampling with ensemble learning","author":"Sikora","year":"2024","journal-title":"Inf. Syst. Front."},{"issue":"8","key":"10.1016\/j.asoc.2025.112819_bib58","doi-asserted-by":"crossref","first-page":"4085","DOI":"10.1007\/s13198-024-02412-w","article-title":"A fused grey wolf and artificial bee colony model for imbalanced data classification problems","volume":"15","author":"Bharti","year":"2024","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"issue":"3","key":"10.1016\/j.asoc.2025.112819_bib59","doi-asserted-by":"crossref","first-page":"4309","DOI":"10.3934\/mbe.2024190","article-title":"A new imbalanced data oversampling method based on bootstrap method and wasserstein generative adversarial network","volume":"21","author":"Hou","year":"2024","journal-title":"Math. Biosci. Eng."},{"issue":"6","key":"10.1016\/j.asoc.2025.112819_bib60","doi-asserted-by":"crossref","first-page":"1-1","DOI":"10.1002\/cpe.7586","article-title":"Undersampling of approaching the classification boundary for imbalance problem","volume":"35","author":"Jiang","year":"2023","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"10.1016\/j.asoc.2025.112819_bib61","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123987","article-title":"IMWMOTE: a novel oversampling technique for fault diagnosis in heterogeneous imbalanced data","volume":"251","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2025.112819_bib62","article-title":"Ensemble synthetic oversampling with pixel pair for class-imbalanced and small-sized hyperspectral data classification","volume":"128","author":"Feng","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.asoc.2025.112819_bib63","article-title":"A survey on unbalanced classification: how can evolutionary computation help?","author":"Pei","year":"2023","journal-title":"IEEE Trans. Evolut. Comput."},{"issue":"6","key":"10.1016\/j.asoc.2025.112819_bib64","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1109\/TAI.2022.3160658","article-title":"On supervised class-imbalanced learning: An updated perspective and some key challenges","volume":"3","author":"Das","year":"2022","journal-title":"IEEE Trans. Artif. Intell."},{"issue":"1","key":"10.1016\/j.asoc.2025.112819_bib65","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1214\/aoms\/1177731944","article-title":"A comparison of alternative tests of significance for the problem of m rankings","volume":"11","author":"Friedman","year":"1940","journal-title":"Ann. Math. Stat."},{"issue":"3","key":"10.1016\/j.asoc.2025.112819_bib66","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s00500-008-0323-y","article-title":"KEEL: a software tool to assess evolutionary algorithms for data mining problems","volume":"13","author":"Alcal\u00e1-Fdez","year":"2009","journal-title":"Soft Comput."},{"key":"10.1016\/j.asoc.2025.112819_bib67","series-title":"A study of cross-validation and bootstrap for accuracy estimation and model selection","author":"Kohavi","year":"1995"},{"key":"10.1016\/j.asoc.2025.112819_bib68","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.patrec.2020.03.004","article-title":"Adjusting the imbalance ratio by the dimensionality of imbalanced data","volume":"133","author":"Zhu","year":"2020","journal-title":"Pattern Recognit. Lett."},{"issue":"3","key":"10.1016\/j.asoc.2025.112819_bib69","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets","volume":"10","author":"Saito","year":"2015","journal-title":"PloS One"},{"issue":"Preprint","key":"10.1016\/j.asoc.2025.112819_bib70","first-page":"1","article-title":"An interpretable decision tree ensemble model for imbalanced credit scoring datasets","author":"My","year":"2023","journal-title":"J. Intell. Fuzzy Syst."},{"key":"10.1016\/j.asoc.2025.112819_bib71","series-title":"2024 IEEE Congress on Evolutionary Computation (CEC)","article-title":"Enhancing Imbalanced Classification with Support Vector Machines via Evolutionary Oversampling Algorithms","author":"Wang","year":"2024"},{"issue":"8","key":"10.1016\/j.asoc.2025.112819_bib72","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.1109\/TCYB.2022.3163974","article-title":"Handling imbalanced classification problems with support vector machines via evolutionary bilevel optimization","volume":"53","author":"Rosales-P\u00e9rez","year":"2022","journal-title":"IEEE Trans. Cybern."},{"issue":"8","key":"10.1016\/j.asoc.2025.112819_bib73","doi-asserted-by":"crossref","first-page":"5273","DOI":"10.1007\/s10994-023-06448-0","article-title":"Solving imbalanced learning with outlier detection and features reduction","volume":"113","author":"Lusito","year":"2024","journal-title":"Mach. Learn."},{"issue":"6","key":"10.1016\/j.asoc.2025.112819_bib74","first-page":"1342","article-title":"Training multi-layer perceptron with artificial algae algorithm","volume":"23","author":"Turkoglu","year":"2020","journal-title":"Eng. Sci. Technol., Int. J."},{"issue":"1","key":"10.1016\/j.asoc.2025.112819_bib75","doi-asserted-by":"crossref","first-page":"39","DOI":"10.2307\/3315656","article-title":"Tables for the friedman rank test","volume":"21","author":"Martin","year":"1993","journal-title":"Can. J. Stat."},{"issue":"2","key":"10.1016\/j.asoc.2025.112819_bib76","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s10115-011-0465-6","article-title":"SMOTE-RS B*\u2009: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory","volume":"33","author":"Ramentol","year":"2012","journal-title":"Knowl. Inf. Syst."},{"issue":"1","key":"10.1016\/j.asoc.2025.112819_bib77","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494625001309?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494625001309?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T17:27:21Z","timestamp":1745602041000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494625001309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":77,"alternative-id":["S1568494625001309"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2025.112819","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A synergistic oversampling technique with differential evolution and safe level synthetic minority oversampling","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2025.112819","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112819"}}