Performance Evaluation of Support Vector Machine: Before and After Image Data Augmentation

Performance Evaluation of Support Vector Machine: Before and After Image Data Augmentation

© 2022 by IJETT Journal
Volume-70 Issue-2
Year of Publication : 2022
Authors : M S Sunitha Patel, Srinath S
DOI :  10.14445/22315381/IJETT-V70I2P230

How to Cite?

M S Sunitha Patel, Srinath S, "Performance Evaluation of Support Vector Machine: Before and After Image Data Augmentation," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 266-274, 2022. Crossref,

Data to train the machine learning algorithms is a fundamental aspect to achieve the desired result for the given problem. Collecting the raw data and creating the datasets, pre-processing the data, and annotating for the given problem is a basic skill in data science engineering. Constructing the right dataset in automotive image processing problems usually takes 50% of the time in machine learning-based problem-solving techniques. Automotive image processing has more challenges considering the diversified weather conditions, road conditions, driving conditions, etc. Collecting diversified images in the automotive domain is one of the challenging tasks for researchers to study and implement machine learning algorithms. Image data augmentation will benefit from making required quantity and quality datasets for the automotive domain. In this paper, an effort has been made to show how to increase dataset size by utilizing image augmentation technique and also to create, test, train, validate the dataset for the multiclass vehicles. By utilizing the image data augmentation technique, validation and test dataset error have been reduced, which finally improved the performance of the machine learning model. Performance evaluation of the machine learning algorithm for the datasets with and without image augmentation has been analyzed. For analysis purposes, a Support Vector Machine (SVM) classifier has been utilized, and performance evaluation has been done using Receiver Operating Characteristics (ROC). Precision, recall, and F1 score has been compared for the dataset with and without image augmentation.

Data Science, Dataset, Image Data Augmentation, Automotive Image Processing, SVM.

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