{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T07:50:08Z","timestamp":1783410608645,"version":"3.54.6"},"reference-count":27,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T00:00:00Z","timestamp":1615766400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2021,4,23]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance. The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset. This issue is known as the imbalance problem, which is one of the most common issues occurring in real-time applications. Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining. Imbalanced data degrades the performance of the classifier by producing inaccurate results.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In the proposed work, a novel fuzzy-based Gaussian synthetic minority oversampling (FG-SMOTE) algorithm is proposed to process the imbalanced data. The mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class datasets. The ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure technique.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC: 93.7%. F1 Score Prediction: 94.2%, Geometric Mean Score: 93.6% predicted from confusion matrix.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>The proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed environment.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced data. FG-SMOTE has aided in balancing minority and majority class datasets.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-12-2020-0202","type":"journal-article","created":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T08:13:20Z","timestamp":1615536800000},"page":"270-287","source":"Crossref","is-referenced-by-count":12,"title":["FG-SMOTE: Fuzzy-based Gaussian synthetic minority oversampling with deep belief networks classifier for skewed class distribution"],"prefix":"10.1108","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5242-0475","authenticated-orcid":false,"given":"Putta","family":"Hemalatha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3246-5243","authenticated-orcid":false,"given":"Geetha Mary","family":"Amalanathan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2021,3,15]]},"reference":[{"issue":"3","key":"key2022121313515789700_ref001","doi-asserted-by":"crossref","first-page":"116","DOI":"10.25046\/aj020316","article-title":"Medical imbalanced data classification","volume":"2","year":"2017","journal-title":"Advances in Science, Technology and Engineering Systems Journal"},{"key":"key2022121313515789700_ref002","first-page":"403","article-title":"Enhanced SMOTE algorithm for classification of imbalanced big-data using random forest","year":"2015"},{"issue":"2","key":"key2022121313515789700_ref003","first-page":"423","article-title":"Enhancing the performance of SMOTE algorithm by using attribute weighting scheme and new selective sampling method for imbalanced data set","volume":"15","year":"2019","journal-title":"International Journal of Innovative Computing Information and Control"},{"key":"key2022121313515789700_ref004","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","year":"2018","journal-title":"Journal of Artificial Intelligence Research"},{"key":"key2022121313515789700_ref005","first-page":"79","year":"2017"},{"key":"key2022121313515789700_ref006","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","year":"2017","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"key2022121313515789700_ref007","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1186\/s40537-019-0274-4","article-title":"Severely imbalanced big data challenges: investigating data sampling approaches","volume":"6","year":"2019","journal-title":"Journal of Big Data"},{"issue":"1","key":"key2022121313515789700_ref008","first-page":"1","article-title":"A novel algorithm for imbalance data classification based on neighborhood hypergraph","volume":"1","year":"2014","journal-title":"Science World Journal"},{"issue":"1","key":"key2022121313515789700_ref010","first-page":"11","article-title":"A novel algorithm for class imbalance learning on big data using under sampling technique","volume":"15","year":"2019","journal-title":"International Journal of Computational Intelligence Research"},{"issue":"10","key":"key2022121313515789700_ref009","first-page":"2407","article-title":"A novel technique on class imbalance big data using analogous over sampling approach","volume":"13","year":"2017","journal-title":"International Journal of Computational Intelligence Research"},{"key":"key2022121313515789700_ref011","first-page":"152","article-title":"Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm","year":"2010"},{"issue":"8","key":"key2022121313515789700_ref012","doi-asserted-by":"crossref","first-page":"3255","DOI":"10.1007\/s13369-016-2179-2","article-title":"A novel algorithm for imbalance data classification based on genetic algorithm improved SMOTE","volume":"41","year":"2016","journal-title":"Arabian Journal for Science and Engineering"},{"issue":"4","key":"key2022121313515789700_ref013","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","year":"2016","journal-title":"Progress in Artificial Intelligence"},{"key":"key2022121313515789700_ref014","article-title":"A hybrid approach using oversampling technique and cost-sensitive learning for bankruptcy prediction","year":"2019","journal-title":"Complexity"},{"issue":"8","key":"key2022121313515789700_ref015","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1016\/j.asoc.2012.03.051","article-title":"A novel algorithm applied to classify unbalanced data","volume":"12","year":"2012","journal-title":"Applied Soft Computing"},{"issue":"4","key":"key2022121313515789700_ref016","doi-asserted-by":"crossref","first-page":"229","DOI":"10.5391\/IJFIS.2017.17.4.229","article-title":"Gaussian-based SMOTE algorithm for solving skewed class distributions","volume":"17","year":"2017","journal-title":"International Journal of Fuzzy Logic and Intelligent Systems"},{"issue":"11","key":"key2022121313515789700_ref017","first-page":"16","volume":"8","year":"2019","journal-title":"International Journal of Engineering Research and Technology"},{"issue":"1","key":"key2022121313515789700_ref018","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s40537-017-0108-1","article-title":"Improved classification of large imbalanced data sets using rationalized technique: updated class purity maximization over_sampling technique (UCPMOT)","volume":"4","year":"2017","journal-title":"Journal of Big Data"},{"issue":"4","key":"key2022121313515789700_ref019","first-page":"47","article-title":"Multi-label classification with PSO based synthetic minority over-sampling technique (PSOSMOTE) for imbalanced samples","volume":"8","year":"2019","journal-title":"International Journal of Recent Technology and Engineering"},{"key":"key2022121313515789700_ref020","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2017.01.078","article-title":"A survey on data preprocessing for data stream mining: current status and future directions","volume":"239","year":"2017","journal-title":"Neurocomputing"},{"key":"key2022121313515789700_ref021","first-page":"431","year":"2018"},{"issue":"12","key":"key2022121313515789700_ref022","first-page":"69","article-title":"Multiclass data imbalance oversampling techniques (MUDIOT) and random selection of features","volume":"8","year":"2019","journal-title":"International Journal of Innovative Technology and Exploring Engineering"},{"key":"key2022121313515789700_ref023","first-page":"240","article-title":"Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","year":"2017"},{"key":"key2022121313515789700_ref024","article-title":"An improved oversampling algorithm based on the samples' selection strategy for classifying imbalanced data","year":"2019","journal-title":"Mathematical Problems in Engineering"},{"key":"key2022121313515789700_ref025","doi-asserted-by":"crossref","first-page":"23537","DOI":"10.1109\/ACCESS.2019.2899467","article-title":"A parameter-free cleaning method for SMOTE in imbalanced classification","volume":"7","year":"2019","journal-title":"IEEE Access"},{"issue":"3","key":"key2022121313515789700_ref026","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1007\/s13042-015-0478-7","article-title":"The classification of imbalanced large data sets based on mapreduce and ensemble of elm classifiers","volume":"8","year":"2017","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"key2022121313515789700_ref027","article-title":"A framework of rebalancing imbalanced healthcare data for rare events' classification: a case of look-alike sound-alike mix-up incident detection","year":"2018","journal-title":"Journal of Healthcare Engineering"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-12-2020-0202\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-12-2020-0202\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:55:09Z","timestamp":1753397709000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/14\/2\/270-287\/125580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,15]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,3,15]]},"published-print":{"date-parts":[[2021,4,23]]}},"alternative-id":["10.1108\/IJICC-12-2020-0202"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-12-2020-0202","relation":{},"ISSN":["1756-378X"],"issn-type":[{"value":"1756-378X","type":"print"}],"subject":[],"published":{"date-parts":[[2021,3,15]]}}}