{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T02:23:26Z","timestamp":1762050206506,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004895","name":"European Social Fund","doi-asserted-by":"publisher","award":["EFOP-3.6.3-VEKOP-16-2017-00002"],"award-info":[{"award-number":["EFOP-3.6.3-VEKOP-16-2017-00002"]}],"id":[{"id":"10.13039\/501100004895","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Data imbalance is a serious problem in machine learning that can be alleviated at the data level by balancing the class distribution with sampling. In the last decade, several sampling methods have been published to address the shortcomings of the initial ones, such as noise sensitivity and incorrect neighbor selection. Based on the review of the literature, it has become clear to us that the algorithms achieve varying performance on different data sets. In this paper, we present a new oversampler that has been developed based on the key steps and sampling strategies identified by analyzing dozens of existing methods and that can be fitted to various data sets through an optimization process. Experiments were performed on a number of data sets, which show that the proposed method had a similar or better effect on the performance of SVM, DTree, kNN and MLP classifiers compared with other well-known samplers found in the literature. The results were also confirmed by statistical tests.<\/jats:p>","DOI":"10.3390\/computers11050073","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T08:21:25Z","timestamp":1651652485000},"page":"73","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Highly Adaptive Oversampling Approach to Address the Issue of Data Imbalance"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4121-4745","authenticated-orcid":false,"given":"Szilvia","family":"Szeghalmy","sequence":"first","affiliation":[{"name":"Faculty of Informatics, University of Debrecen, H-4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6893-3067","authenticated-orcid":false,"given":"Attila","family":"Fazekas","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, University of Debrecen, H-4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez, A., Garc\u00eda, S., Galar, M., Prati, R.C., Krawczyk, B., and Herrera, F. (2018). Learning from Imbalanced Data Sets, Springer.","DOI":"10.1007\/978-3-319-98074-4"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, C., Xin, Y., Li, X., Yang, Y., and Chen, Y. (2020). A heterogeneous ensemble learning framework for spam detection in social networks with imbalanced data. Appl. Sci., 10.","DOI":"10.3390\/app10030936"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"115492","DOI":"10.1016\/j.eswa.2021.115492","article-title":"A minority oversampling approach for fault detection with heterogeneous imbalanced data","volume":"184","author":"Liu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107932","DOI":"10.1016\/j.knosys.2021.107932","article-title":"A Quadruplet Deep Metric Learning model for imbalanced time-series fault diagnosis","volume":"238","author":"Gui","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1472-6947-11-51","article-title":"Predicting disease risks from highly imbalanced data using random forest","volume":"11","author":"Khalilia","year":"2011","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103089","DOI":"10.1016\/j.jbi.2018.12.003","article-title":"A comprehensive data level analysis for cancer diagnosis on imbalanced data","volume":"90","author":"Fotouhi","year":"2019","journal-title":"J. Biomed. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.neunet.2020.07.033","article-title":"Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results","volume":"131","author":"Su","year":"2020","journal-title":"Neural Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1109\/LRA.2021.3089999","article-title":"Multi-Sensor Guided Hand Gesture Recognition for a Teleoperated Robot Using a Recurrent Neural Network","volume":"6","author":"Qi","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1109\/JBHI.2019.2963048","article-title":"A multimodal wearable system for continuous and real-time breathing pattern monitoring during daily activity","volume":"24","author":"Qi","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, P., and Hoi, S.C. (2013, January 11\u201314). Cost-sensitive online active learning with application to malicious URL detection. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487647"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Weiss, G.M. (2013). Foundations of imbalanced learning. InImbalanced Learning: Foundations, Algorithms, and Applications, Wiley-IEEE Press.","DOI":"10.1002\/9781118646106.ch2"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1109\/TNNLS.2017.2732482","article-title":"Cost-sensitive learning of deep feature representations from imbalanced data","volume":"29","author":"Khan","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"Guo","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113026","DOI":"10.1016\/j.eswa.2019.113026","article-title":"Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced data","volume":"158","author":"Florencia","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_16","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":"ref_17","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary","volume":"61","author":"Garcia","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Han, H., Wang, W.Y., and Mao, B.H. (2005, January 23\u201326). Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. Proceedings of the International Conference on Intelligent Computing, Hefei, China.","DOI":"10.1007\/11538059_91"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ma, L., and Fan, S. (2017). CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1578-z"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Le, T., Le Son, H., Vo, M.T., Lee, M.Y., and Baik, S.W. (2018). A cluster-based boosting algorithm for bankruptcy prediction in a highly imbalanced dataset. Symmetry, 10.","DOI":"10.3390\/sym10070250"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.ins.2021.02.056","article-title":"A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data","volume":"572","author":"Xu","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1350008","DOI":"10.1142\/S0218213013500085","article-title":"Synthetic oversampling of instances using clustering","volume":"22","author":"Sanchez","year":"2013","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ins.2014.08.051","article-title":"SMOTE\u2013IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering","volume":"291","author":"Luengo","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1007\/s10489-011-0287-y","article-title":"DBSMOTE: Density-based synthetic minority over-sampling technique","volume":"36","author":"Bunkhumpornpat","year":"2012","journal-title":"Appl. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.21629\/JSEE.2019.06.12","article-title":"Over-sampling algorithm for imbalanced data classification","volume":"30","author":"Xu","year":"2019","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"694809","DOI":"10.1155\/2013\/694809","article-title":"A novel boundary oversampling algorithm based on neighborhood rough set model: NRSBoundary-SMOTE","volume":"2013","author":"Hu","year":"2013","journal-title":"Math. Probl. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, S., Liang, Y., Ma, L., and He, Y. (2009, January 28\u201330). MSMOTE: Improving classification performance when training data is imbalanced. Proceedings of the 2009 Second International Workshop on Computer Science and Engineering, Qingdao, China.","DOI":"10.1109\/WCSE.2009.756"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Pan, T., Zhang, C., and Yang, J. (2021). A new oversampling method based on the classification contribution degree. Symmetry, 13.","DOI":"10.3390\/sym13020194"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2018104826","DOI":"10.1016\/j.knosys.2019.06.034","article-title":"Improving interpolation-based oversampling for imbalanced data learning","volume":"187","author":"Zhu","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","article-title":"Asymptotic properties of nearest neighbor rules using edited data","volume":"3","author":"Wilson","year":"1972","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_31","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008, January 1\u20138). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bunkhumpornpat, C., Sinapiromsaran, K., and Lursinsap, C. (2009, January 27\u201330). Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Bangkok, Thailand.","DOI":"10.1007\/978-3-642-01307-2_43"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1145\/1007730.1007737","article-title":"Class imbalances versus small disjuncts","volume":"6","author":"Jo","year":"2004","journal-title":"ACM Sigkdd Explor. Newsl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cateni, S., Colla, V., and Vannucci, M. (2011, January 22\u201324). Novel resampling method for the classification of imbalanced datasets for industrial and other real-world problems. Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, Cordoba, Spain.","DOI":"10.1109\/ISDA.2011.6121689"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s10618-012-0295-5","article-title":"Training and assessing classification rules with imbalanced data","volume":"28","author":"Menardi","year":"2014","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_36","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."},{"key":"ref_37","unstructured":"Cieslak, D.A., Chawla, N.V., and Striegel, A. (2006, January 10\u201312). Combating imbalance in network intrusion datasets. Proceedings of the GrC, Atlanta, GA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhou, B., Yang, C., Guo, H., and Hu, J. (2013, January 4\u20139). A quasi-linear SVM combined with assembled SMOTE for imbalanced data classification. Proceedings of the 2013 International Joint Conference on Neural Networks, Dallas, TX, USA.","DOI":"10.1109\/IJCNN.2013.6707035"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Koto, F. (2014, January 18\u201319). SMOTE-Out, SMOTE-Cosine, and Selected-SMOTE: An enhancement strategy to handle imbalance in data level. Proceedings of the International Conference on Advanced Computer Science and Information System, Tanjung Priok, Indonesia.","DOI":"10.1109\/ICACSIS.2014.7065849"},{"key":"ref_40","unstructured":"Chen, L., Cai, Z., Chen, L., and Gu, Q. (2010, January 9\u201310). A novel differential evolution-clustering hybrid resampling algorithm on imbalanced datasets. Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining, Phuket, Thailand."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Laurikkala, J. (2001, January 1\u20134). Improving identification of difficult small classes by balancing class distribution. Proceedings of the Conference on Artificial Intelligence in Medicine in Europe, Cascais, Portugal.","DOI":"10.1007\/3-540-48229-6_9"},{"key":"ref_42","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the KDD, Portland, OR, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, N.R., and Lee, J.H. (2015, January 8\u201310). An over-sampling technique with rejection for imbalanced class learning. Proceedings of the Ninth International Conference on Ubiquitous Information Management and Communication, ACM, Bali, Indonesia.","DOI":"10.1145\/2701126.2701181"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.artmed.2005.03.002","article-title":"Learning from imbalanced data in surveillance of nosocomial infection","volume":"37","author":"Cohen","year":"2006","journal-title":"Artif. Intell. Med."},{"key":"ref_45","unstructured":"De la Calleja, J., Fuentes, O., and Gonz\u00e1lez, J. (2008, January 15\u201317). Selecting Minority Examples from Misclassified Data for Over-Sampling. Proceedings of the FLAIRS Conference, Coconut Grove, FL, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., Hinneburg, A., and Keim, D.A. (2001). On the Surprising Behavior of Distance Metrics in High Dimensional Space. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/3-540-44503-X_27"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s11390-007-9054-2","article-title":"Improving software quality prediction by noise filtering techniques","volume":"22","author":"Khoshgoftaar","year":"2007","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"105662","DOI":"10.1016\/j.asoc.2019.105662","article-title":"An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets","volume":"83","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gazzah, S., and Amara, N.E.B. (2008, January 16\u201319). New oversampling approaches based on polynomial fitting for imbalanced data sets. Proceedings of the 2008 the Eighth Iapr International Workshop on Document Analysis Systems, Nara, Japan.","DOI":"10.1109\/DAS.2008.74"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Barua, S., Islam, M.M., and Murase, K. ProWSyn: Proximity weighted synthetic oversampling technique for imbalanced data set learning. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Gold Coast, QLD, Australia, 14\u201317 April 2013, Springer.","DOI":"10.1007\/978-3-642-37456-2_27"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cao, Q., and Wang, S. (2011, January 26\u201327). Applying over-sampling technique based on data density and cost-sensitive svm to imbalanced learning. Proceedings of the 2011 International Conference on Information Management, Innovation Management and Industrial Engineering, Shenzhen, China.","DOI":"10.1109\/ICIII.2011.276"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sandhan, T., and Choi, J.Y. (2014, January 24\u201328). Handling imbalanced datasets by partially guided hybrid sampling for pattern recognition. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.258"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1515\/amcs-2017-0050","article-title":"CCR: A combined cleaning and resampling algorithm for imbalanced data classification","volume":"27","author":"Koziarski","year":"2017","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"ref_54","first-page":"1","article-title":"Lvq-smote\u2013learning vector quantization based synthetic minority over\u2013sampling technique for biomedical data","volume":"6","author":"Nakamura","year":"2013","journal-title":"J. BioData Min."},{"key":"ref_55","first-page":"255","article-title":"KEEL Data-Mining Software Tool: Data set repository, integration of algorithms and Experimental analysis framework","volume":"17","author":"Fernandez","year":"2011","journal-title":"J. Mult. Valued Log. Soft Comput."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.neucom.2019.06.100","article-title":"Smote-variants: A python implementation of 85 minority oversampling techniques","volume":"366","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_57","unstructured":"(2022, February 10). UCI Machine Learning Repository: Data Sets. Available online: https:\/\/archive.ics.uci.edu\/ml\/datasets.php."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/5\/73\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:05:53Z","timestamp":1760137553000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/5\/73"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":57,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["computers11050073"],"URL":"https:\/\/doi.org\/10.3390\/computers11050073","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2022,5,4]]}}}