Fuzzy and SVM Based Classification Model to Classify Spectral Objects in Sloan Digital Sky

Arodh Lal Karn, Carlos Andres Tavera Romero, Sudhakar Sengan*, Abolfazl Mehbodniya, Julian L. Webber, Denis A. Pustokhin, Frank Detlef Wende

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)101276-101291
Number of pages16
JournalIEEE Access
Publication statusPublished - 2022


  • Sloan digital sky
  • astronomical
  • fuzzy control
  • fuzzy logic
  • kernel principal component analysis
  • machine learning
  • nearest neighbor
  • support vector machine


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