Optimal progressive classification study using SMOTE-SVM for stages of lung disease
Data used in big data applications are typically kept in decentralized computing resources in the real world, which has an impact on the design of artificial intelligence algorithms. When there are significantly more observations from one class than from another, the dataset is said to be imbalanced...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-10-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2023.2218167 |
| _version_ | 1797245044828143616 |
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| author | R. Sujitha B. Paramasivan |
| author_facet | R. Sujitha B. Paramasivan |
| author_sort | R. Sujitha |
| collection | DOAJ |
| description | Data used in big data applications are typically kept in decentralized computing resources in the real world, which has an impact on the design of artificial intelligence algorithms. When there are significantly more observations from one class than from another, the dataset is said to be imbalanced. Therefore, in this work, the study elaborates the model as SMOTE-SVM which resolves imbalance issues in sampling the data and improves overall accuracy to 94%. The model deploys K-nearest neighbours to compute the difference between samples and to balance the samples, it computes the kernel space. Further, to optimize the classification, GWO optimizer merges with SMOTE-SVM to achieve enhanced performance. GWO (Grey Wolf Optimizer) induces greedy selection to perform optimization among classification. It is important to remember that grey wolves have a flexible social structure that might change the hierarchy. As the mobilization continues, the grey wolves are reconstructed with the distance between them and their prey, or more specifically, in accordance with the resultant value of the fitness. In addition, to prove the efficiency, the following performance metrics are measured-Overall Accuracy, Classification Accuracy, AUC and ROC. |
| first_indexed | 2024-03-10T15:57:46Z |
| format | Article |
| id | doaj.art-5223534319054c08aab1af5cc6ff89cf |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-04-24T19:20:39Z |
| publishDate | 2023-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-5223534319054c08aab1af5cc6ff89cf2024-03-25T18:18:03ZengTaylor & Francis GroupAutomatika0005-11441848-33802023-10-0164480781410.1080/00051144.2023.2218167Optimal progressive classification study using SMOTE-SVM for stages of lung diseaseR. Sujitha0B. Paramasivan1Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, IndiaDepartment of Information Technology, National Engineering College (Autonomous), Kovilpatti, IndiaData used in big data applications are typically kept in decentralized computing resources in the real world, which has an impact on the design of artificial intelligence algorithms. When there are significantly more observations from one class than from another, the dataset is said to be imbalanced. Therefore, in this work, the study elaborates the model as SMOTE-SVM which resolves imbalance issues in sampling the data and improves overall accuracy to 94%. The model deploys K-nearest neighbours to compute the difference between samples and to balance the samples, it computes the kernel space. Further, to optimize the classification, GWO optimizer merges with SMOTE-SVM to achieve enhanced performance. GWO (Grey Wolf Optimizer) induces greedy selection to perform optimization among classification. It is important to remember that grey wolves have a flexible social structure that might change the hierarchy. As the mobilization continues, the grey wolves are reconstructed with the distance between them and their prey, or more specifically, in accordance with the resultant value of the fitness. In addition, to prove the efficiency, the following performance metrics are measured-Overall Accuracy, Classification Accuracy, AUC and ROC.https://www.tandfonline.com/doi/10.1080/00051144.2023.2218167Classificationoptimizationgrey-wolf optimizerminority sampleslung cancer |
| spellingShingle | R. Sujitha B. Paramasivan Optimal progressive classification study using SMOTE-SVM for stages of lung disease Automatika Classification optimization grey-wolf optimizer minority samples lung cancer |
| title | Optimal progressive classification study using SMOTE-SVM for stages of lung disease |
| title_full | Optimal progressive classification study using SMOTE-SVM for stages of lung disease |
| title_fullStr | Optimal progressive classification study using SMOTE-SVM for stages of lung disease |
| title_full_unstemmed | Optimal progressive classification study using SMOTE-SVM for stages of lung disease |
| title_short | Optimal progressive classification study using SMOTE-SVM for stages of lung disease |
| title_sort | optimal progressive classification study using smote svm for stages of lung disease |
| topic | Classification optimization grey-wolf optimizer minority samples lung cancer |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2023.2218167 |
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