Evaluation and Comparison of Machine Learning Algorithms for Effective Image Classification with Fault-Tolerance - 2024

Aug 2, 2026

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Image classification is central to computer vision and supports applications that span e-commerce and medical imaging. This study evaluates traditional machine learning classifiers and implements fault-tolerance mechanisms to improve robustness under exceptions and errors. Models including K-Nearest Neighbors, Decision Trees, Random Forest, XGBoost, Support Vector Machine, Logistic Regression, and Naive Bayes are compared on Fashion MNIST and CIFAR-10. Results show ensemble dominance, especially XGBoost, alongside a fault-tolerant framework delivering a high recovery rate and strong production reliability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages53

Reading Time2 h 14 min

AccessFree to download

Abstract

Keywords

1. Introduction

Evaluation and Comparison of Machine Learning Algorithms for Effective Image Classification with Fault-Tolerance Sithembiso Dyubele [ctheradyubele@gmail.com](ctheradyubele@gmail.com) Department of Information Systems Durban University of Technology Durban, South Africa Noxolo Pretty Cele [noxolocele53@gmail.com](noxolocele53@gmail.com) Department of Information Systems Durban University of Technology Durban, South Africa Lubabalo Mbangata [lubabalo.mbangata@gmail.com](lubabalo.mbangata@gmail.com) Department of Information Systems Durban University of Technology Durban, South Africa [Phirime Monyeki](Phirime Monyeki phirimemonyeki@gmail.com)[ phirimemonyeki@gmail.com](Phirime Monyeki phirimemonyeki@gmail.com) Department of Information Systems Durban University of Technology Durban, South Africa Corresponding Author: Sithembiso Dyubele Copyright © 2024 Sithembiso Dyubele, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Image classification is critical in computer vision, with numerous applications ranging frome-commerce to medical imaging. This study provides a comprehensive evaluation of traditional machine learning algorithms for image classification, implementing and analysing novel fault tolerance mechanisms amongst these algorithms. The authors compared the performance ofK-Nearest Neighbors (KNN), Decision Trees, Random Forest, and XGBoost on both Fashion MNIST and CIFAR-10 datasets. The comparison was extended to include Support Vector Machine (SVM), Logistic Regression, and Naive Bayes classifiers in order to expand the evaluation of these models on the indicated datasets. Key findings demonstrated the superiority of ensemble methods, particularly XGBoost, which achieved 89.31% of accuracy on Fashion MNIST and 54.93% on CIFAR-10, consistently outperforming other models across various configurations. Random Forest exhibited robust performance as the secondbest model, reaching 87.42% and 51.64% of accuracy on the respective datasets. The significant performance gap between datasets demonstrated the challenges that traditional machine learning models face with complex image data. Implementing the fault tolerance framework in this study has also shown a remarkable effectiveness, achieved a 94.6% recovery rate while maintaining model accuracy within 0.1% of standard implementations. This was achieved with minimal computational overhead (2.3% of training time and 1.8% of memory usage), 3006 Citation: Sithembiso Dyubele, et al. Evaluation and Comparison of Machine Learning Algorithms for Effective Image Classification with Fault-Tolerance. Advances in Artificial Intelligence and Machine Learning. 2024;4(4):174 . [https://www.oajaiml.com/ | December 2024](https://www.oajaiml.com/ | December 2024) Sithembiso Dyubele, et al. making it highly practical for production deployments. The system significantly reduced operational failures, decreasing crashes from 5.2 to 0.3 per day and increasing average uptime from 4.3 to 12.0 hours. The study also reveals important insights regarding model scalability and resource requirements, with memory usage varying significantly across models (325MB to 8,923MB) . These findings provide valuable guidance for practitioners in selecting and implementing machine learning models for image classification tasks, particularly in scenarios where both performance and system reliability are critical. This research contributes to the field by demonstrating the feasibility of implementing robust fault tolerance in machine learning systems without compromising accuracy while also providing comprehensive performance comparisons across different model architectures and dataset complexities. The developed framework serves as a foundation for building more reliable machine-learning systems for rea

FAQs

Which machine learning algorithms are compared for image classification?

The study compares K-Nearest Neighbors, Decision Trees, Random Forest, XGBoost, Support Vector Machine, Logistic Regression, and Naive Bayes on image classification tasks.

Which datasets are used to evaluate model performance?

Performance is evaluated on Fashion MNIST and CIFAR-10 datasets.

How does the fault-tolerance framework affect robustness and recovery?

The proposed fault-tolerance approach achieves a 94.6% recovery rate while keeping model accuracy within 0.1% of standard implementations, with low computational overhead.

Aug 2, 2026

Upload by Sage