Assessing the Dimensionality Reduction of the Geospatial Dataset Using Principal Component Analysis (PCA) and Its Impact on the Accuracy and Performance of Ensembled and Non-ensembled Algorithms

Farkhanda Abbas; Feng Zhang; Javed Iqbal; Fazila Abbas; Abdulwahed Fahad Alrefaei; Mohammed Albeshr

doi:10.20944/preprints202307.0529.v1

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Assessing the Dimensionality Reduction of the Geospatial Dataset Using Principal Component Analysis (PCA) and Its Impact on the Accuracy and Performance of Ensembled and Non-ensembled Algorithms

This version is not peer-reviewed.

Farkhanda Abbas^*,

Feng Zhang,Javed Iqbal,

Fazila Abbas,

Abdulwahed Fahad Alrefaei

Mohammed Albeshr

Farkhanda Abbas^*,

Feng Zhang,Javed Iqbal,

Fazila Abbas,

Abdulwahed Fahad Alrefaei

Mohammed Albeshr

This version is not peer-reviewed.

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Submitted:

07 July 2023

Posted:

10 July 2023

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Computer Science and Mathematics - Artificial Intelligence and Machine Learning

Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

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Assessing the Dimensionality Reduction of the Geospatial Dataset Using Principal Component Analysis (PCA) and Its Impact on the Accuracy and Performance of Ensembled and Non-ensembled Algorithms

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