Improved Technique for Dimensionality Reduction: Star and Quasar Classification with Typical Testors

Mateo Martínez-Mejía; Julio Ibarra-Fiallo

doi:10.1007/978-3-031-47724-9_19

Improved Technique for Dimensionality Reduction: Star and Quasar Classification with Typical Testors

Mateo Martínez-Mejía^*
, Julio Ibarra-Fiallo

^*Corresponding author for this work

Matemáticas

Universidad San Francisco de Quito

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This work compares the feature selection technique offered by Testor Theory, using the Yablonski and Compatible Sets (YYC) algorithm for the calculation of a fitting typical testor, against the dimensionality reduction given by the principal component analysis (PCA) calculation. Using the results obtained from the previous algorithms and using a Support Vector Machine (SVM) as a classification model, we acquire the classification results of stars and quasars. Lastly, we analyze the advantages shown by typical testors from the experimental results obtained over the ones obtained from the PCA technique.

Original language	English
Title of host publication	Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2
Editors	Kohei Arai
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	275-290
Number of pages	16
ISBN (Print)	9783031477232
DOIs	https://doi.org/10.1007/978-3-031-47724-9_19
State	Published - 2024
Event	Intelligent Systems Conference, IntelliSys 2023 - Amsterdam, Netherlands Duration: 7 Sep 2023 → 8 Sep 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	823 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	Intelligent Systems Conference, IntelliSys 2023
Country/Territory	Netherlands
City	Amsterdam
Period	7/09/23 → 8/09/23

Keywords

Classification Model
Dimensionality Reduction
Testor Theory
Typical Testor

Access to Document

10.1007/978-3-031-47724-9_19

Cite this

Martínez-Mejía, M., & Ibarra-Fiallo, J. (2024). Improved Technique for Dimensionality Reduction: Star and Quasar Classification with Typical Testors. In K. Arai (Ed.), Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2 (pp. 275-290). (Lecture Notes in Networks and Systems; Vol. 823 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-47724-9_19

Martínez-Mejía, Mateo ; Ibarra-Fiallo, Julio. / Improved Technique for Dimensionality Reduction : Star and Quasar Classification with Typical Testors. Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2. editor / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2024. pp. 275-290 (Lecture Notes in Networks and Systems).

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Martínez-Mejía, M & Ibarra-Fiallo, J 2024, Improved Technique for Dimensionality Reduction: Star and Quasar Classification with Typical Testors. in K Arai (ed.), Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2. Lecture Notes in Networks and Systems, vol. 823 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 275-290, Intelligent Systems Conference, IntelliSys 2023, Amsterdam, Netherlands, 7/09/23. https://doi.org/10.1007/978-3-031-47724-9_19

Improved Technique for Dimensionality Reduction: Star and Quasar Classification with Typical Testors. / Martínez-Mejía, Mateo; Ibarra-Fiallo, Julio.
Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2. ed. / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2024. p. 275-290 (Lecture Notes in Networks and Systems; Vol. 823 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This work compares the feature selection technique offered by Testor Theory, using the Yablonski and Compatible Sets (YYC) algorithm for the calculation of a fitting typical testor, against the dimensionality reduction given by the principal component analysis (PCA) calculation. Using the results obtained from the previous algorithms and using a Support Vector Machine (SVM) as a classification model, we acquire the classification results of stars and quasars. Lastly, we analyze the advantages shown by typical testors from the experimental results obtained over the ones obtained from the PCA technique.

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Martínez-Mejía M, Ibarra-Fiallo J. Improved Technique for Dimensionality Reduction: Star and Quasar Classification with Typical Testors. In Arai K, editor, Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2. Springer Science and Business Media Deutschland GmbH. 2024. p. 275-290. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-47724-9_19