Fuzzy and SVM Based Classification Model to Classify Spectral Objects in Sloan Digital Sky
| dc.contributor.author | Karn, Arodh Lal | |
| dc.contributor.author | Tavera Romero, Carlos Andres | |
| dc.contributor.author | Sengan, Sudhakar | |
| dc.contributor.author | Mehbodniya, Abolfazl | |
| dc.contributor.author | Webber, Julian L. | |
| dc.contributor.author | Pustokhin, Denis A. | |
| dc.contributor.author | Wende, Frank-Detlef | |
| dc.date.accessioned | 2025-07-04T15:48:23Z | |
| dc.date.available | 2025-07-04T15:48:23Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | The Sloan Digital Sky Survey (SDSS) comprises about one billion objects classified spectrometrically. Because astronomical datasets are so enormous, manually classifying them is nearly impossible - a huge dataset results in class imbalance and overfitting. We recommend a framework in this research study that overcomes these constraints. The framework uses a hybrid Synthetic Minority Oversampling Technique + Edited Nearest Neighbor (SMOTE + ENN) balancer. The balanced dataset is then used to extract features via a non-linear algorithm using Kernel Principal Component Analysis (KPCA). The features are then passed into the proposed Int-T2-Fuzzy Support Vector Machine classifier, which uses a modified type reducer and inference engine to achieve more precise categorization. Using the Sloan Digital Sky Survey dataset and a number of evaluation metrics, the SMOTE+ENN model's performance is measured. The research shows that the model does a good job. | |
| dc.identifier.citation | Karn, A. L., Romero, C. A. T., Sengan, S., Mehbodniya, A., Webber, J. L., Pustokhin, D. A., & Wende, F. D. (2022). Fuzzy and SVM Based Classification Model to Classify Spectral Objects in Sloan Digital Sky. IEEE Access, 10. https://doi.org/10.1109/ACCESS.2022.3207480 | |
| dc.identifier.issn | 21693536 | |
| dc.identifier.uri | https://repositorio.usc.edu.co/handle/20.500.12421/7168 | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | astronomical | |
| dc.subject | fuzzy control | |
| dc.subject | fuzzy logic | |
| dc.subject | kernel principal component analysis | |
| dc.subject | machine learning | |
| dc.subject | nearest neighbor | |
| dc.subject | Sloan digital sky | |
| dc.subject | support vector machine | |
| dc.title | Fuzzy and SVM Based Classification Model to Classify Spectral Objects in Sloan Digital Sky | |
| dc.type | Article |
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