Multi-algorithm comparison and performance evaluation

Ruiting Zhao; Jingru Ding; Tianqi Song; Anqi Ye

doi:10.1117/12.3050805

27 September 2024 Multi-algorithm comparison and performance evaluation

Ruiting Zhao, Jingru Ding, Tianqi Song, Anqi Ye

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132810B (2024) https://doi.org/10.1117/12.3050805
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

This report selects the rice dataset from UCL to compare the performance of several classic classification algorithms in the rice classification task, including linear discriminant analysis, logistic regression, K-nearest neighbor KNN classification, and naive Bayes classification. Through data preprocessing and feature engineering, we run naive Bayes classifiers under different prior distributions and analyze the classification results in detail. In addition, we select evaluation criteria such as accuracy, precision, recall, and F1 score to compare and discuss the effectiveness of each classification algorithm. The final results show that the choice of different prior distributions also has a certain impact on the classification results, and the classification effects of linear discriminant analysis, logistic regression, and Gaussian Bayes are better. This article details the experimental process and results analysis, providing some reference value for how to classify rice.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruiting Zhao, Jingru Ding, Tianqi Song, and Anqi Ye "Multi-algorithm comparison and performance evaluation", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132810B (27 September 2024); https://doi.org/10.1117/12.3050805

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KEYWORDS

Statistical analysis

Data analysis

Image classification

Machine learning

Performance modeling

Education and training

Agriculture

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