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

被引：2

作者：

Karn, Arodh Lal ^{[1
]}

Tavera Romero, Carlos Andres ^{[2
]}

Sengan, Sudhakar ^{[3
]}

Mehbodniya, Abolfazl ^{[4
]}

Webber, Julian L. ^{[4
]}

Pustokhin, Denis A. ^{[5
]}

Wende, Frank-Detlef ^{[6
]}

机构：

[1] Xian Jiaotong Liverpool Univ, Sch Math & Phys, Dept Financial & Actuarial Math, Suzhou 215123, Peoples R China

[2] Univ Santiago Cali, Fac Engn, COMBA R&D Lab, Cali 76001, Colombia

[3] PSN Coll Engn & Technol, Dept Comp Sci & Engn, Tirunelveli 627152, India

[4] Kuwait Coll Sci & Technol, Dept Elect & Commun Engn, Kuwait, Kuwait

[5] State Univ Management, Dept Logist, Moscow 109542, Russia

[6] Financial Univ Govt Russian Federat, Fac Econ & Business, Dept Logist & Mkt, Moscow 125993, Russia

来源：

IEEE ACCESS | 2022年 / 10卷

关键词：

Support vector machines; Machine learning; Feature extraction; Telescopes; Principal component analysis; Fuzzy logic; Fuzzy sets; Astronomy; Sloan digital sky; fuzzy logic; fuzzy control; support vector machine; nearest neighbor; machine learning; astronomical; kernel principal component analysis; SUPPORT VECTOR MACHINES; AUTOMATED CLASSIFICATION; DATA SETS; GALAXIES; SMOTE;

D O I：

10.1109/ACCESS.2022.3207480

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：101276 / 101291

页数：16

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