Comparison of Support Vector Machine Classifier and Naïve Bayes Classifier on Road Surface Type Classification

Susi Marianingsih; Fitri Utaminingrum

doi:10.1109/SIET.2018.8693113

Comparison of Support Vector Machine Classifier and Naïve Bayes Classifier on Road Surface Type Classification

Susi Marianingsih
, Fitri Utaminingrum

Faculty of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

25 Citations (Scopus)

Abstract

This study describes the comparison of road surface classification results using Support Vector Machine (SVM) classification and Naive Bayes classification. The dataset in this study is a collection of sub-images (750 images) from the road area of the image from Google Street View. From the dataset, 600 images as data training and 150 images as data testing. Texture features are extracted from road surface images in the dataset and then we build SVM and Naive Bayes classifiers to classify road surface images in 3 categories, asphalt, gravel, and paving. Evaluation of performance classification using precision, recall, f-measure, and accuracy. The results show that SVM classifier accuracy better than Naive Bayes classifier.

Original language	English
Title of host publication	3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	48-53
Number of pages	6
ISBN (Electronic)	9781538674079
DOIs	https://doi.org/10.1109/SIET.2018.8693113
Publication status	Published - 2 Jul 2018
Event	3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Malang, Indonesia Duration: 10 Nov 2018 → 12 Nov 2018

Publication series

Name	3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings

Conference

Conference	3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018
Country/Territory	Indonesia
City	Malang
Period	10/11/18 → 12/11/18

Keywords

GLCM
Naïve Bayes
SVM
Texture analysis

Access to Document

10.1109/SIET.2018.8693113

Cite this

Marianingsih, S., & Utaminingrum, F. (2018). Comparison of Support Vector Machine Classifier and Naïve Bayes Classifier on Road Surface Type Classification. In 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings (pp. 48-53). Article 8693113 (3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIET.2018.8693113

Marianingsih, Susi ; Utaminingrum, Fitri. / Comparison of Support Vector Machine Classifier and Naïve Bayes Classifier on Road Surface Type Classification. 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 48-53 (3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings).

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title = "Comparison of Support Vector Machine Classifier and Na{\"i}ve Bayes Classifier on Road Surface Type Classification",

abstract = "This study describes the comparison of road surface classification results using Support Vector Machine (SVM) classification and Naive Bayes classification. The dataset in this study is a collection of sub-images (750 images) from the road area of the image from Google Street View. From the dataset, 600 images as data training and 150 images as data testing. Texture features are extracted from road surface images in the dataset and then we build SVM and Naive Bayes classifiers to classify road surface images in 3 categories, asphalt, gravel, and paving. Evaluation of performance classification using precision, recall, f-measure, and accuracy. The results show that SVM classifier accuracy better than Naive Bayes classifier.",

keywords = "GLCM, Na{\"i}ve Bayes, SVM, Texture analysis",

author = "Susi Marianingsih and Fitri Utaminingrum",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 ; Conference date: 10-11-2018 Through 12-11-2018",

year = "2018",

month = jul,

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doi = "10.1109/SIET.2018.8693113",

language = "English",

series = "3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Marianingsih, S & Utaminingrum, F 2018, Comparison of Support Vector Machine Classifier and Naïve Bayes Classifier on Road Surface Type Classification. in 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings., 8693113, 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 48-53, 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018, Malang, Indonesia, 10/11/18. https://doi.org/10.1109/SIET.2018.8693113

Comparison of Support Vector Machine Classifier and Naïve Bayes Classifier on Road Surface Type Classification. / Marianingsih, Susi; Utaminingrum, Fitri.
3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 48-53 8693113 (3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Comparison of Support Vector Machine Classifier and Naïve Bayes Classifier on Road Surface Type Classification

AU - Marianingsih, Susi

AU - Utaminingrum, Fitri

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N2 - This study describes the comparison of road surface classification results using Support Vector Machine (SVM) classification and Naive Bayes classification. The dataset in this study is a collection of sub-images (750 images) from the road area of the image from Google Street View. From the dataset, 600 images as data training and 150 images as data testing. Texture features are extracted from road surface images in the dataset and then we build SVM and Naive Bayes classifiers to classify road surface images in 3 categories, asphalt, gravel, and paving. Evaluation of performance classification using precision, recall, f-measure, and accuracy. The results show that SVM classifier accuracy better than Naive Bayes classifier.

AB - This study describes the comparison of road surface classification results using Support Vector Machine (SVM) classification and Naive Bayes classification. The dataset in this study is a collection of sub-images (750 images) from the road area of the image from Google Street View. From the dataset, 600 images as data training and 150 images as data testing. Texture features are extracted from road surface images in the dataset and then we build SVM and Naive Bayes classifiers to classify road surface images in 3 categories, asphalt, gravel, and paving. Evaluation of performance classification using precision, recall, f-measure, and accuracy. The results show that SVM classifier accuracy better than Naive Bayes classifier.

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KW - Naïve Bayes

KW - SVM

KW - Texture analysis

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M3 - Conference contribution

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Marianingsih S, Utaminingrum F. Comparison of Support Vector Machine Classifier and Naïve Bayes Classifier on Road Surface Type Classification. In 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 48-53. 8693113. (3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings). doi: 10.1109/SIET.2018.8693113

Comparison of Support Vector Machine Classifier and Naïve Bayes Classifier on Road Surface Type Classification

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