Karn, Arodh LalTavera Romero, Carlos AndresSengan, SudhakarMehbodniya, AbolfazlWebber, Julian L.Pustokhin, Denis A.Wende, Frank-Detlef2025-07-042025-07-042022Karn, 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.2022.320748021693536https://repositorio.usc.edu.co/handle/20.500.12421/7168The 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.enastronomicalfuzzy controlfuzzy logickernel principal component analysismachine learningnearest neighborSloan digital skysupport vector machineArticle