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A novel gannet optimization algorithm is applied to feature selection in hyperspectral images

Published: 26 December 2024 Publication History

Abstract

Feature Selection (FS) plays a pivotal role in Hyperspectral Image (HSI) classification, albeit hindered by the curse of dimensionality problem. Swarm Intelligence and Evolutionary Algorithms (SIEA) have emerged as effective approaches to tackle this challenge. In our investigation, we leverage the newly proposed Gannet Optimization algorithm for feature selection, coupled with the KNN algorithm for hyperspectral image classification. Notably, our experimental findings showcase the superior performance of the Gannet algorithm over several traditional optimization algorithms, as evidenced by enhanced classification accuracy.

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ICEA '23: Proceedings of the 2023 International Conference on Intelligent Computing and Its Emerging Applications
December 2023
175 pages
ISBN:9798400709050
DOI:10.1145/3659154
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 December 2024

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Author Tags

  1. Gannet Optimization
  2. Feature selection
  3. Classification
  4. Hyper-spectral images
  5. KNN

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