{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:28:18Z","timestamp":1782318498282,"version":"3.54.5"},"reference-count":52,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:00:00Z","timestamp":1660521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Information"],"abstract":"<jats:p>Class imbalance is one of the significant challenges in classification problems. The uneven distribution of data samples in different classes may occur due to human error, improper\/unguided collection of data samples, etc. The uneven distribution of class samples among classes may affect the classification accuracy of the developed model. The main motivation behind this study is the design and development of methodologies for handling class imbalance problems. In this study, a new variant of the synthetic minority oversampling technique (SMOTE) has been proposed with the hybridization of particle swarm optimization (PSO) and Egyptian vulture (EV). The proposed method has been termed SMOTE-PSOEV in this study. The proposed method generates an optimized set of synthetic samples from traditional SMOTE and augments the five datasets for verification and validation. The SMOTE-PSOEV is then compared with existing SMOTE variants, i.e., Tomek Link, Borderline SMOTE1, Borderline SMOTE2, Distance SMOTE, and ADASYN. After data augmentation to the minority classes, the performance of SMOTE-PSOEV has been evaluated using support vector machine (SVM), Na\u00efve Bayes (NB), and k-nearest-neighbor (k-NN) classifiers. The results illustrate that the proposed models achieved higher accuracy than existing SMOTE variants.<\/jats:p>","DOI":"10.3390\/info13080386","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T20:58:08Z","timestamp":1660597088000},"page":"386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["RETRACTED: A Tailored Particle Swarm and Egyptian Vulture Optimization-Based Synthetic Minority-Oversampling Technique for Class Imbalance Problem"],"prefix":"10.3390","volume":"13","author":[{"given":"Subhashree","family":"Rout","sequence":"first","affiliation":[{"name":"School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar 751024, Odisha, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1207-0757","authenticated-orcid":false,"given":"Pradeep","family":"Mallick","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar 751024, Odisha, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3345-651X","authenticated-orcid":false,"given":"Annapareddy","family":"V. 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Reddy","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Lakireddy Bali Reddy College of Engineering, Mylavaram 521230, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3949-0302","authenticated-orcid":false,"given":"Sachin","family":"Kumar","sequence":"additional","affiliation":[{"name":"Big Data and Machine Learning Lab, South Ural State University, 454080 Chelyabinsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107965","DOI":"10.1016\/j.patcog.2021.107965","article-title":"A Review of Methods for Imbalanced Multi-Label Classification","volume":"118","author":"Tarekegn","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.patrec.2017.08.002","article-title":"Measuring the class-imbalance extent of multi-class problems","volume":"98","author":"Inza","year":"2017","journal-title":"Pattern Recognit. 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