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

Mateo Martínez-Mejía, Julio Ibarra-Fiallo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaIntelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2
EditoresKohei Arai
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas275-290
Número de páginas16
ISBN (versión impresa)9783031477232
DOI
EstadoPublicada - 2024
EventoIntelligent Systems Conference, IntelliSys 2023 - Amsterdam, Países Bajos
Duración: 7 sep. 20238 sep. 2023

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen823 LNNS
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

ConferenciaIntelligent Systems Conference, IntelliSys 2023
País/TerritorioPaíses Bajos
CiudadAmsterdam
Período7/09/238/09/23

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