Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
Series Vol. 2 , 22 March 2023
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A Comparison on Machine Learning Classifiers related to Traffic Sign Recognition
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Abstract
With the rapid development of machine learning and the automotive industry, the industry of autonomous driving continues to grow. At the same time, governments have new regulations on autonomous driving, which tells us that reliable systems have become more and more critical while developing autonomous driving. In this paper, I use three different classifiers, which are Logistic Regression (LR), Random Forest (RF), and neural network (Multilayer Perceptron), to do the traffic sign recognition tasks and set the best parameters for every classifier. I train three classifiers with the best parameters and estimate using cross-value methods. Finally, I compared the performance, which indicates Random Forest Classifier has the best result among the three classifiers.
Keywords
Logistic Regression, Random Forest., Machine Learning
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
- ISBN (Print)
- 978-1-915371-19-5
- ISBN (Online)
- 978-1-915371-20-1
- Published Date
- 22 March 2023
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/2/20220567
- Copyright
- 22 March 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited