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Applying rough-fuzzy theory to the imbalanced data learning problem could be a promising research direction that generates the synthetic data and removes the outliers. The proposed work identifies the positive, boundary, and negative regions of the target set using the rough set theory and removes the objects in the negative region as outliers. It also explores the positive and boundary regions of the rough set by applying the fuzzy theory to generate the samples of the minority class and remove the samples of the majority class. Thus the proposed rough-fuzzy approach performs both oversampling and undersampling to handle the imbalanced class problem. The experimental results demonstrate that the novel technique allows qualitative and quantitative data handling.<\/jats:p>","DOI":"10.3390\/axioms12040345","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T14:19:33Z","timestamp":1680272373000},"page":"345","source":"Crossref","is-referenced-by-count":1,"title":["Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem"],"prefix":"10.3390","volume":"12","author":[{"given":"Mehwish","family":"Naushin","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah 711103, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9886-1735","authenticated-orcid":false,"given":"Asit Kumar","family":"Das","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah 711103, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9746-6557","authenticated-orcid":false,"given":"Janmenjoy","family":"Nayak","sequence":"additional","affiliation":[{"name":"P.G Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo (MSCB) University, Baripada, Mayurbhanj 757003, India"}]},{"given":"Danilo","family":"Pelusi","sequence":"additional","affiliation":[{"name":"CosteSant\u2019agostino Campus, Department of Communication Sciences, University of Teramo, 64100 Teramo, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Priscilla, C.V., and Prabha, D.P. (2020, January 20\u201322). Influence of optimizing xgboost to handle class imbalance in credit card fraud detection. Proceedings of the 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India.","DOI":"10.1109\/ICSSIT48917.2020.9214206"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rousso, R., Katz, N., Sharon, G., Glizerin, Y., Kosman, E., and Shuster, A. (2022). Automatic recognition of oil spills using neural networks and classic image processing. Water, 14.","DOI":"10.3390\/w14071127"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rodda, S., and Erothi, U.S.R. (2016, January 3\u20135). Class imbalance problem in the network intrusion detection systems. Proceedings of the 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India.","DOI":"10.1109\/ICEEOT.2016.7755181"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"84897","DOI":"10.1109\/ACCESS.2019.2924923","article-title":"A MCDM-based evaluation approach for imbalanced classification methods in financial risk prediction","volume":"7","author":"Song","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.eswa.2007.10.042","article-title":"Imbalanced text classification: A term weighting approach","volume":"36","author":"Liu","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.ins.2020.01.032","article-title":"Adaptive weighted over-sampling for imbalanced datasets based on density peaks clustering with heuristic filtering","volume":"519","author":"Tao","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tasci, E., Zhuge, Y., Camphausen, K., and Krauze, A.V. (2022). Bias and Class Imbalance in Oncologic Data\u2014Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets. Cancers, 14.","DOI":"10.3390\/cancers14122897"},{"key":"ref_8","first-page":"521","article-title":"Dealing with the class imbalance problem in the detection of fake job descriptions","volume":"68","author":"Vo","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"115067","DOI":"10.1016\/j.eswa.2021.115067","article-title":"Sequential targeting: A continual learning approach for data imbalance in text classification","volume":"179","author":"Jang","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.neucom.2017.05.066","article-title":"A hybrid method based on ensemble WELM for handling multi class imbalance in cancer microarray data","volume":"266","author":"Liu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s10115-011-0465-6","article-title":"Smote-rs b*: 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."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Srinilta, C., and Kanharattanachai, S. (2021, January 1\u20133). Application of natural neighbor-based algorithm on oversampling smote algorithms. Proceedings of the 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST), Bangkok, Thailand.","DOI":"10.1109\/ICEAST52143.2021.9426310"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"116045","DOI":"10.1016\/j.trac.2020.116045","article-title":"New data preprocessing trends based on ensemble of multiple preprocessing techniques","volume":"132","author":"Mishra","year":"2020","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hasib, K.M., Iqbal, M., Shah, F.M., Mahmud, J.A., Popel, M.H., Showrov, M., Hossain, I., Ahmed, S., and Rahman, O. (2020). A survey of methods for managing the classification and solution of data imbalance problem. arXiv.","DOI":"10.3844\/jcssp.2020.1546.1557"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","article-title":"Learning from imbalanced data: Open challenges and future directions","volume":"5","author":"Krawczyk","year":"2016","journal-title":"Prog. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"30655","DOI":"10.1109\/ACCESS.2022.3158977","article-title":"SMOTified-GAN for class imbalanced pattern classification problems","volume":"10","author":"Sharma","year":"2022","journal-title":"IEEE Access"},{"key":"ref_19","first-page":"1573","article-title":"Sentimental analysis from imbalanced code-mixed data using machine learning approaches","volume":"41","author":"Srinivasan","year":"2021","journal-title":"Distrib. Parallel Databases"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"114750","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":"ref_21","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s00779-019-01332-y","article-title":"GAN-based imbalanced data intrusion detection system","volume":"25","author":"Lee","year":"2021","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"35995","DOI":"10.1007\/s11042-020-09138-4","article-title":"Synthetic minority oversampling in addressing imbalanced sarcasm detection in social media","volume":"79","author":"Banerjee","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11036","DOI":"10.1109\/ACCESS.2022.3141776","article-title":"A Hybrid GAN-Based Approach to Solve Imbalanced Data Problem in Recommendation Systems","volume":"10","author":"Shafqat","year":"2022","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112","DOI":"10.3844\/jcssp.2021.112.122","article-title":"Sentimental Analysis on Health-Related Information with Improving Model Performance using Machine Learning","volume":"17","author":"Yafooz","year":"2021","journal-title":"J. Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.neunet.2020.10.004","article-title":"CEGAN: Classification Enhancement Generative Adversarial Networks for unraveling data imbalance problems","volume":"133","author":"Suh","year":"2021","journal-title":"Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.eij.2022.05.006","article-title":"The impact of synthetic text generation for sentiment analysis using GAN based models","volume":"23","author":"Imran","year":"2022","journal-title":"Egypt. Inform. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4663","DOI":"10.1007\/s40747-021-00608-2","article-title":"ETHOS: A multi-label hate speech detection dataset","volume":"8","author":"Mollas","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2019.01.041","article-title":"Feature selection for imbalanced data based on neighborhood rough sets","volume":"483","author":"Chen","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.knosys.2019.03.001","article-title":"Multi-imbalance: An open-source software for multi-class imbalance learning","volume":"174","author":"Zhang","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_30","unstructured":"Behmanesh, M., Adibi, P., and Karshenas, H. (2021). Weighted least squares twin support vector machine with fuzzy rough set theory for imbalanced data classification. arXiv."},{"key":"ref_31","first-page":"118","article-title":"A fuzzy similarity based classification with Archimedean-Dombi aggregation operator","volume":"1","author":"Saha","year":"2022","journal-title":"J. Intell Manag. Decis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","article-title":"A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance","volume":"505","author":"Elreedy","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113504","DOI":"10.1016\/j.eswa.2020.113504","article-title":"NI-MWMOTE: An improving noise-immunity majority weighted minority oversampling technique for imbalanced classification problems","volume":"158","author":"Wei","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1109\/TKDE.2014.2324567","article-title":"RACOG and wRACOG: Two probabilistic oversampling techniques","volume":"27","author":"Das","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"444","DOI":"10.23883\/IJRTER.2017.3168.0UWXM","article-title":"A review on imbalanced data handling using undersampling and oversampling technique","volume":"3","author":"Shelke","year":"2017","journal-title":"Int. J. Recent Trends Eng. Res."},{"key":"ref_36","first-page":"3649406","article-title":"A comprehensive investigation of the performances of different machine learning classifiers with SMOTE-ENN oversampling technique and hyperparameter optimization for imbalanced heart failure dataset","volume":"2022","author":"Faisal","year":"2022","journal-title":"Sci. Program."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Swana, E.F., Doorsamy, W., and Bokoro, P. (2022). Tomek link and SMOTE approaches for machine fault classification with an imbalanced dataset. Sensors, 22.","DOI":"10.3390\/s22093246"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.knosys.2018.05.044","article-title":"Fuzzy rule-based oversampling technique for imbalanced and incomplete data learning","volume":"158","author":"Liu","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2465","DOI":"10.1007\/s10489-020-01644-0","article-title":"Oversampling technique based on fuzzy representativeness difference for classifying imbalanced data","volume":"50","author":"Ren","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TNNLS.2018.2855446","article-title":"Active learning from imbalanced data: A solution of online weighted extreme learning machine","volume":"30","author":"Yu","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., and Napolitano, A. (2008, January 8\u201311). RUSBoost: Improving classification performance when training data is skewed. Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA.","DOI":"10.1109\/ICPR.2008.4761297"},{"key":"ref_43","unstructured":"Kazerouni, A., Zhao, Q., Xie, J., Tata, S., and Najork, M. (2020). Active learning for skewed data sets. arXiv."},{"key":"ref_44","unstructured":"Qu, W., Yan, D., Sang, Y., Liang, H., Kitsuregawa, M., and Li, K. (2008, January 26\u201328). A novel Chi2 algorithm for discretization of continuous attributes. Proceedings of the Progress in WWW Research and Development: 10th Asia-Pacific Web Conference, APWeb 2008, Shenyang, China."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lavangnananda, K., and Chattanachot, S. (2017, January 1\u20134). Study of discretization methods in classification. Proceedings of the 2017 9th International Conference on Knowledge and Smart Technology (KST), Chonburi, Thailan.","DOI":"10.1109\/KST.2017.7886082"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Das, A.K., Chakrabarty, S., Pati, S.K., and Sahaji, A.H. (2012, January 10\u201312). Applying restrained genetic algorithm for attribute reduction using attribute dependency and discernibility matrix. Proceedings of the Wireless Networks and Computational Intelligence: 6th International Conference on Information Processing, ICIP, Bangalore, India.","DOI":"10.1007\/978-3-642-31686-9_36"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"211","DOI":"10.6029\/smartcr.2014.03.007","article-title":"Feature selection: A literature review","volume":"4","author":"Kumar","year":"2014","journal-title":"SmartCR"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Basu, S., Das, S., Ghatak, S., and Das, A.K. (2017, January 23\u201325). Strength pareto evolutionary algorithm based gene subset selection. Proceedings of the 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Andhra Pradesh, India.","DOI":"10.1109\/ICBDACI.2017.8070813"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104021","DOI":"10.1016\/j.landusepol.2019.104021","article-title":"Automated valuation model based on fuzzy and rough set theory for real estate market with insufficient source data","volume":"87","author":"Janowski","year":"2019","journal-title":"Land Use Policy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"109092","DOI":"10.1016\/j.knosys.2022.109092","article-title":"A noise-aware fuzzy rough set approach for feature selection","volume":"250","author":"Yang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"11089","DOI":"10.1007\/s10489-021-03028-4","article-title":"A fuzzy rough set approach to hierarchical feature selection based on Hausdorff distance","volume":"52","author":"Qiu","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_52","first-page":"35","article-title":"A study on rough set theory based dynamic reduct for classification system optimization","volume":"5","author":"Sengupta","year":"2014","journal-title":"Int. J. Artif. Intell. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"77","DOI":"10.4018\/jcini.2010040106","article-title":"Feature reduction with inconsistency","volume":"4","author":"Liu","year":"2010","journal-title":"Int. J. Cogn. Informatics Nat. Intell. IJCINI"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MCI.2018.2881643","article-title":"Fuzzy clustering: A historical perspective","volume":"14","author":"Ruspini","year":"2019","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2902","DOI":"10.1109\/TFUZZ.2021.3097806","article-title":"An unsupervised fuzzy clustering approach for early screening of COVID-19 from radiological images","volume":"30","author":"Ding","year":"2021","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1007\/s00180-020-00999-9","article-title":"What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?","volume":"36","author":"Marcot","year":"2021","journal-title":"Comput. Stat."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Shukla, S. (2016, January 27\u201328). Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. Proceedings of the 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, India.","DOI":"10.1109\/IACC.2016.25"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s10472-017-9564-8","article-title":"A Bayesian interpretation of the confusion matrix","volume":"81","author":"Caelen","year":"2017","journal-title":"Ann. Math. Artif. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.eswa.2018.01.008","article-title":"An overlap-sensitive margin classifier for imbalanced and overlapping data","volume":"98","author":"Lee","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Platt","year":"2001","journal-title":"Neural Comput."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ins.2019.08.062","article-title":"Neighbourhood-based undersampling approach for handling imbalanced and overlapped data","volume":"509","author":"Vuttipittayamongkol","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kokkotis, C., Giarmatzis, G., Giannakou, E., Moustakidis, S., Tsatalas, T., Tsiptsios, D., Vadikolias, K., and Aggelousis, N. (2022). An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data. Diagnostics, 12.","DOI":"10.3390\/diagnostics12102392"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1136\/emermed-2017-206735","article-title":"What is an ROC curve?","volume":"34","author":"Hoo","year":"2017","journal-title":"Emerg. Med. J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.ins.2021.04.016","article-title":"An improvement of rough sets\u2019 accuracy measure using containment neighborhoods with a medical application","volume":"569","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Al-Shami, T.M., and Alshammari, I. (2022). Rough sets models inspired by supra-topology structures. Artif. Intell. Rev., 1\u201329.","DOI":"10.1007\/s10462-022-10346-7"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"107680","DOI":"10.1016\/j.asoc.2021.107680","article-title":"Data augmentation by guided deep interpolation","volume":"111","author":"Szlobodnyik","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3544558","article-title":"A survey on data augmentation for text classification","volume":"55","author":"Bayer","year":"2022","journal-title":"ACM Comput. Surv."}],"container-title":["Axioms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/4\/345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T14:56:24Z","timestamp":1680274584000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/4\/345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,31]]},"references-count":67,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["axioms12040345"],"URL":"https:\/\/doi.org\/10.3390\/axioms12040345","relation":{},"ISSN":["2075-1680"],"issn-type":[{"value":"2075-1680","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,31]]}}}