{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:10:08Z","timestamp":1753884608166,"version":"3.41.2"},"reference-count":21,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Image Grap."],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:p> Data imbalance occurring among multiclass datasets is very common in real-world applications. Existing studies reveal that various attempts were made in the past to overcome this multiclass imbalance problem, which is a severe issue related to the typical supervised machine learning methods such as classification and regression. But, still there exists a need to handle the imbalance problem efficiently as the datasets include both safe and unsafe minority samples. Most of the widely used oversampling techniques like SMOTE and its variants face challenges in replicating or generating the new data instances for balancing them across multiple classes, particularly when the imbalance is high and the number of rare samples count is too minimal thus leading the classifier to misclassify the data instances. To lessen this problem, we proposed a new data balancing method namely a two-stage iterative ensemble method to tackle the imbalance in multiclass environment. The proposed approach focuses on the rare minority sample\u2019s influence on learning from imbalanced datasets and the main idea of the proposed approach is to balance the data without any change in class distribution before it gets trained by the learner such that it improves the learner\u2019s learning process. Also, the proposed approach is compared against two widely used oversampling techniques and the results reveals that the proposed approach shows a much significant improvement in the learning process among the multiclass imbalanced data. <\/jats:p>","DOI":"10.1142\/s0219467822500255","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T01:52:02Z","timestamp":1633571522000},"source":"Crossref","is-referenced-by-count":1,"title":["Performance Analysis of Two-Stage Iterative Ensemble Method over Random Oversampling Methods on Multiclass Imbalanced Datasets"],"prefix":"10.1142","volume":"22","author":[{"given":"S.","family":"Sridhar","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, SRM Institute of Science & Technology, Vadapalani Campus, Chennai 600026, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Kalaivani","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai 602105, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"key":"S0219467822500255BIB001","first-page":"7","volume":"74","author":"Skryjomski P.","year":"2017","journal-title":"Proc. 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