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Principal Directions-Based Data Classification Optimized by Genetic Algorithms and K-Nearest Neighbors

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Doru Constantin
Costel Bălcău
Published in
Multi-disciplinary Trends in Artificial Intelligence
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the optimization of the Principal Directions algorithm using genetic algorithms and K-Nearest Neighbors (KNN) for enhanced data classification. The study focuses on the integration of these techniques to improve the selection of informative eigenvectors and evaluate their efficiency in digital image classification tasks. The experimental analysis involves evaluating the algorithm on various image datasets, including MNIST, Fashion-MNIST, CIFAR-10, and COVID-19 X-Ray images. The results demonstrate the algorithm's competitive performance compared to standard methods, with a strong balance between accuracy and computational cost. Statistical validation through ANOVA and Wilcoxon Signed-Rank tests confirms the algorithm's effectiveness, showing significant improvements over traditional KNN and genetic algorithm-based classifiers. The chapter concludes with a discussion on the potential future applications of this approach in multilayer neural network architectures for enhanced image recognition capabilities.
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previous chapter Bridging Language and Vision: Fine-Tuning Latent Diffusion Models for Robust Text-to-Image Generation
next chapter FedIncSparse: A Federated Learning Framework with Top-K Sparse Delta Transmission and Incremental Updates
Title
Principal Directions-Based Data Classification Optimized by Genetic Algorithms and K-Nearest Neighbors
Authors
Doru Constantin
Costel Bălcău
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Multi-disciplinary Trends in Artificial Intelligence

Print ISBN: 978-981-9549-56-6

Electronic ISBN: 978-981-9549-57-3

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-4957-3_14
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