{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:22:31Z","timestamp":1777890151349,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Web Intelligence"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>Imbalanced data classification (IDC) presents a significant challenge in data mining (DM), as it frequently occurs in various real-world areas with profound implications for highly skewed databases. IDC revolves around the task of learning from data characterized by a substantial imbalance in the number of samples across its different classes. Hence the Polar-CanisFel (PCF) Optimization-deep ensemble model is designed to address imbalanced big data issues, incorporating the SMOTE technique for rebalancing the dataset. This ensemble classifier leverages a deep convolutional neural network (DCNN), Long Short-Term Memory (LSTM), and Gated Recurrent Neural Network (GRNN) architectures for effective data classification. For the Heart Failure Prediction Dataset, the model reaches an accuracy of 96.35%, sensitivity of 94.54%, and specificity of 96.11%. Further, the accuracy of 95.91%, sensitivity of 95.87%, and specificity of 94.79% are obtained concerning the Stroke Prediction dataset. Finally, when applied to the Hepatitis-C prediction dataset, the model attains an accuracy of 92.79%, sensitivity of 92.90%, and specificity of 92.63% during 90% of training.<\/jats:p>","DOI":"10.3233\/web-230248","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:12:00Z","timestamp":1722942720000},"page":"255-274","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A framework of Polar CanisFel optimization-based deep ensemble classifier with graph embedding for imbalanced data classification"],"prefix":"10.1177","volume":"23","author":[{"given":"Vikas Gajananrao","family":"Bhowate","sequence":"first","affiliation":[{"name":"Department of Information Technology, St. Vincent Pallotti College of Engineering &amp; Technology Gavsi Manapur, Wardha Road, Nagpur 440018, Maharashtra, India"}]},{"given":"T Hanumantha","family":"Reddy","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Rao Bahadur Y Mahabaleswarappa College of Engineering (RYMEC), Ballari 583104, Karnataka, India"}]}],"member":"179","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Ahmed A.M. 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